# Research Papers — 2026-08 · bundle page 68 of 240 > Full-text markdown of papers #3351–3400 of 11995 published in 2026-08, newest first. AI-readable mirrors of the full 11995-paper 2026-08 archive (50 papers per file). Canonical page: https://aipod.dev/papers/archive/2026-08/page/68 [← Previous bundle](https://aipod.dev/papers/archive/2026-08/page/67.md) [Next bundle →](https://aipod.dev/papers/archive/2026-08/page/69.md) ### Object-Uni: A Unified Model for Object-Centric Spatial Understanding and Controllable Generation Authors: Mining Tan, Yinuo Wang, Ziqi Zhou, Weize Quan, Sifei Li, Jingdong Chen, DanDan Zheng, Libin Wang, Weiming Dong Published: 2026-08-24 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2608.22757v1 Unified models for visual understanding and generation have made rapid progress, yet they still lack the ability to understand and manipulate the spatial states of object instances. Existing models can describe objects in natural language, but they struggle to precisely represent continuous object poses and generate geometrically consistent images under target viewpoints. To mitigate this, we propose \emph{Object-Uni}, a unified model for object-centric spatial understanding and controllable generation. Specifically, we formulate object-centric spatial intelligence as a unified problem connecting pose perception, spatial reasoning, pose-conditioned generation, and object-centric novel view synthesis. We treat object pose as an explicit geometric variable shared by understanding and generation, rather than merely a prediction label or control signal. To make pose usable by multimodal large language models, we propose a viewpoint-based orientation abstraction that maps orientation into structured viewpoint descriptions while preserving continuous geometric supervision. We further construct an object-centric spatial benchmark (UniSpatial-80K) and train a unified model with an object-token-grounded pose anchor to associate each instance with its pose state. Experiments show that our model improves object-level pose understanding and pose-controllable generation, moving unified models from describing objects toward manipulating spatial states. --- ### Beyond Factual Knowledge: Benchmarking and Learning Step-Level Procedural Rule Reasoning in Large Language Models Authors: Bohan Yu, Pengfei Cao, Chen Han, Chenxi Zhou, Zhiheng Zhang, Zhiyang Xie, Wenhao Teng, Xiangwen Liao, Jun Zhao, Kang Liu Published: 2026-08-24 Categories: cs.CL arXiv: https://arxiv.org/abs/2608.22753v1 Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided procedural rules at scale. To evaluate this capability, we introduce RuleWorld, a large-scale benchmark that reformulates rules as globally reusable abstract units rather than instance-specific facts. In RuleWorld, several scenarios, including single-rule, parallel multi-rule, and multi-hop reasoning, are settled for comprehensive evaluation. We further propose DynaRule, an end-to-end framework that injects the given rules into the KV cache and turns retrieval into an internal, learnable, step-wise process. Specifically, DynaRule employs Stacked Step-Level Attention Training with a special <search> token to enable dynamic rule re-attention and updating during inference. In this way, the model can re-attend to the most relevant rules at each step, dynamically replacing outdated ones to support more stable multi-step reasoning. Experiments on RuleWorld show that existing LLMs face challenges under large rule pools, while DynaRule improves average QA accuracy by up to 19 points and achieves over 85% Recall@1 at 10K rules, outperforming strong baselines by large margins. We make our code and dataset available here: https://github.com/SharkSpicy-NLP/Beyond-Factual-Knowledge. --- ### The Compaction Cliff in Long-Running AI Agent Memory Authors: Saber Zerhoudi, Jelena Mitrovic, Michael Granitzer Published: 2026-08-24 Categories: cs.AI, cs.IR arXiv: https://arxiv.org/abs/2608.22752v1 A safety rule and an episodic log compete for the same tokens in an AI agent's context. When the budget overflows, both are summarized at the same rate; only the rule needs exact wording to remain enforceable. On 20 production agent configurations, Claude Code's /compact prompt on Sonnet 4.6 preserves 53\% of safety rules after one compaction round and 10\% after five. We name this the Compaction Cliff. We address it with Knowledge Triage, a framework that classifies each line of an agent's knowledge base by type and routes each type through its own retention policy. Three deterministic operators implement this triage across the three context-management operations: TypeCompact rewrites items in place under per-type fidelity, TypeDecompose partitions a topic too large to compact safely, replicating in-scope safety rules across partitions, and TypeRetrieve fetches items from external storage with in-scope rules pinned ahead of relevance. On five public corpora, TypeCompact preserves 2--4$\times$ more safety rules than the strongest single-shot LLM compactor at every ratio, with 96\% recall over five rounds. TypeDecompose reaches 0\% locality violations against 93\% under uniform partitioning. TypeRetrieve reaches 100\% recall@50 against 73\% for the best single-shot LLM retriever. On three downstream behavioral benchmarks, we outperform the production Sonnet compactor on medical compliance (paired McNemar $p < 10^{-8}$ on preservation, $N = 200$), the full-policy and hierarchical baselines on retail task pass rate ($p < 0.01$, $N = 115$), and the hierarchical compaction on the airline domain ($p = 0.024$). We release AgentArtifactCorpus (396{,}934 agent configurations from 54{,}628 public GitHub repositories), the classifier, and the reference implementation. --- ### MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models Authors: Zhekai Wang, Haoxiang Huang, Xiang Liu, Zhikang Chen, Yueqing Sun, Qi Gu, Shiji Zhou, Miao Liu, Sen Cui Published: 2026-08-24 Categories: cs.LG arXiv: https://arxiv.org/abs/2608.22750v1 Object-centric world models forecast future videos by evolving a set of entity slots, but the variables receiving dynamics supervision are often unconstrained visual features. We introduce \method{}, a mask-grounded soft-Hamiltonian world model that makes its position-like state explicitly depend on slot-owned image support. A frozen video-slot encoder produces slots and masks; spatial moments of mask-owned support form a canonical state $Q$, temporal differences form $P$, and a learned energy supplies a soft directional bias to a bounded learned increment. Decoder-relevant appearance and identity are stored separately in a causal visual context. A gated composer and bounded residual then combine this context with the propagated phase state to reconstruct decoder-compatible slots. On OBJ3D, given six observed frames and evaluated over the following 30 frames, \method{} reduces LPIPS by 25.0\% and spatial MSE by 33.7\% relative to the strongest object-centric baseline. On CLEVRER, given six observed frames and evaluated over the following ten frames, the corresponding reductions are 14.5\% and 18.7\%. Horizon-resolved visual and object-state measurements show that the complete model accumulates error more slowly throughout the 30-frame closed-loop rollout. Project page:https://github.com/moshwm-anon/-moshwm-anon.github.io. --- ### Generative Neural Networks for Sinkhorn Distributionally Robust Hypothesis Testing Authors: Fenglin Zhang, Teyan Liu, Jie Wang Published: 2026-08-24 Categories: stat.ML, cs.LG, math.OC arXiv: https://arxiv.org/abs/2608.22746v1 This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust detector against least-favorable distributions in Sinkhorn discrepancy-based ambiguity sets centered at the empirical distributions. Existing approaches solve this problem by solving large-scale conic programs, which are not scalable. To overcome this, we propose a generative framework that learns least-favorable distributions and supports efficient training and end-to-end sampling. For the Sinkhorn discrepancy-based ambiguity sets, we first derive an equivalent conditional-KL-divergence representation with respect to kernel-smoothed reference distributions. This property allows us to prove strong duality for both constrained and unconstrained minimax SDRHT formulations. Based on the closed-form optimal detector and Brenier's theorem, we reformulate the max-min dual formulation as a maximization problem over convex potentials whose gradients characterize invertible transport maps between kernel-smoothed distributions and their least-favorable counterparts. We efficiently approximate these potentials using Hyper Input Convex Neural Networks (HyCNNs) equipped with stochastic gradient estimators and prove the representation power of HyCNNs and the distributional universality of their induced transport maps. Numerical results show that the proposed method achieves superior accuracy and robustness across different sample sizes and dimensions, while avoiding the scalability limitations of classical SDRHT methods. --- ### DiaRelay: Relaying Dialogue Context with a Constant-Size Memory for Emotion Recognition in Conversation Authors: Zihao Zhou, Bin Yang, Jinghui Qin, Kebing Jin Published: 2026-08-24 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2608.22745v1 Emotion Recognition in Conversation (ERC) requires models to identify subtle emotional cues that are often distributed across distant dialogue turns. Existing methods typically incorporate dialogue history through a fixed context window. However, short windows discard potentially useful long-range evidence, while enlarging the window repeatedly re-encodes overlapping utterances, increases computational and memory costs, and may introduce irrelevant context. Moreover, commonly used parameter-efficient adaptation methods, such as LoRA, mainly introduce fixed low-rank transformations in the feature space and do not explicitly maintain a dialogue-level state or condition their transformations on the evolving conversational context. To address these limitations, we propose a lightweight adapter, DiaRelay, to enable LLMs to explicitly maintain a dialogue-level memory for accurate ERC. Based on LoRA, DiaRelay introduces two extra tightly collaborative components, Selective Relay Memory Transition and Dual-axis Relay Memory Read. Selective Relay Memory Transition progressively aggregates useful historical evidence into a bounded relay memory and propagates it across successive utterance predictions. This allows earlier emotional cues to influence later predictions after they leave the local context window, without re-encoding the complete dialogue history or expanding the backbone context length. Dual-axis Relay Memory Read uses the propagated memory to dynamically modulate low-rank feature transformations, enabling context-dependent representation adaptation without test-time gradient updates. Extensive experiments show that DiaRelay can achieve SOTA weighted F1 and accuracy on MELD while obtaining competitive results on IEMOCAP with only an extra 7.1M trainable parameters, indicating the effectiveness and generalizability of our DiaRelay in enhancing LLM-based emotional understanding. --- ### Seeing the Unseen: Semantic-in-Gaussian for Sparse-View 3D Generalization Authors: Zeyang Bai, Yunpeng Wang, Yunbiao Wang, Jun Xiao Published: 2026-08-24 Categories: cs.CV arXiv: https://arxiv.org/abs/2608.22740v1 Generalizable 3D Gaussian Splatting (G-3DGS) has emerged as a promising approach for novel view synthesis undersparse-view settings. However, existing frameworks remain restricted by pixel-aligned Gaussian estimation, whichstruggles in partially observed or occluded regions and often leads to incomplete surfaces or structural collapse. Toaddress these challenges, we propose SeeU (Seeing the Unseen), a novel G-3DGS framework. We frame its core design asSemantic-in-Gaussian: semantic-conditioned refinement in Gaussian space. Specifically, we introduce a Cross-viewEntropy-Aware (CEA) module that aggregates multi-view semantic and geometric cues into compact embeddings. Theseembeddings guide the Conditional Gaussian Transformer, which applies residual updates to coarse Gaussians, helpingrecover under-constrained regions of partially observed structures while preserving surface consistency. Comprehensiveexperiments on multiple benchmarks demonstrate that SeeU consistently improves rendering quality and structuralcompleteness while retaining efficient feed-forward inference. Especially under challenging extrapolation settings,SeeU achieves an average improvement of 2.44 dB in PSNR compared to recent SOTA G-3DGS methods. --- ### LLM-Based Selection of Incongruent Verbal and Nonverbal Behavior for Virtual Humans Authors: Parisa Ghanad Torshizi, Stacy Marsella Published: 2026-08-24 Categories: cs.AI, cs.HC, cs.RO arXiv: https://arxiv.org/abs/2608.22731v1 Nonverbal behavior generation systems for virtual agents often take an utterance as input and generate nonverbal behaviors that emphasize or illustrate the content of the verbal channel. However, human nonverbal behavior is shaped by more than the content of the speech. It is also influenced by speaker roles, interpersonal relationships, social context, and the cognitive and emotional states of the interactants. As a result, the nonverbal channel may reinforce, weaken, qualify, or even contradict the verbal channel. It may also reveal internal states that are hidden or only indirectly implied in speech, including emotional "leakage" that may be incidental to the immediate interaction. Modeling this richer relationship between verbal and nonverbal behavior is important for designing virtual agents that exhibit realistic, human-like behavior. It is especially critical in training contexts that require nuanced social interpretation, such as counseling simulations involving virtual patients. Drawing on Ekman's framework of verbal nonverbal relationships, we propose a taxonomy of categories in which mismatches between verbal and nonverbal behavior can occur. We then examine alternative approaches for realizing these behaviors using large language models, focusing on whether LLMs can select contextually appropriate mismatched verbal and nonverbal behaviors from a given dialogue and social interaction context. Finally, we evaluate the resulting behaviors in a human-subject study, assessing whether context-driven nonverbal behavior, when embodied in a virtual human, produces the intended effects on observers. --- ### Spiking Neural Networks for Continuous Control: Neuromorphic Reinforcement Learning in Conventional Computing Authors: Jessica Hunter, Md Maruf Hossain Shuvo, Krishna Roy Published: 2026-08-24 Categories: cs.LG, cs.NE arXiv: https://arxiv.org/abs/2608.22729v1 Reinforcement learning (RL) algorithms have made strides over the past decade applying them to a wide range of problems and control tasks. However, the deployment of RL on neuromorphic hardware for continuous control tasks remains under-validated. Namely it is unclear whether replacing a conventional actor network with a spiking neural network (SNN) affects the performance of an agent before any hardware-specific benefits manifest. We provide a systematic validation of a minimal, neuromorphically viable spiking actor variant of Soft Actor-Critic (SAC) on conventional hardware, establishing a baseline for future neuromorphic RL research. In this paper, we propose the Spiking Actor Network Soft Actor Critic (SANSAC) to address the use of RL frameworks in continuous environments, designed as a framework that can be implemented on neuromorphic hardware. We compare a traditional Soft Actor Critic (SAC) network to SANSAC in a traditional computer. We demonstrate the near equivalent performance of SANSAC and SAC, while addressing the impact of hidden dimensions. Our results demonstrate the viability of SNN based algorithms in complex continuous environments, as well as competitive performance to traditional neural networks in traditional computers, providing a basis to continue exploring the use of SNNs in continuous RL frameworks. --- ### HAWKEYE: Seeing One Layer Deeper -- A Cohesion-Aware Structural Channel for Temporal Link Prediction Authors: Jiacheng Ding, Xiaofei Zhang Published: 2026-08-24 Categories: cs.DB, cs.LG arXiv: https://arxiv.org/abs/2608.22727v1 State-of-the-art temporal-link-prediction (TLP) models are, in essence, multi-channel information aggregators: they combine an interaction-history channel, a time-encoding channel, and a structure channel. The first two have been refined relentlessly; the structure channel remains a crude afterthought -- DyGFormer encodes it as a 1--2-bit neighbour-cooccurrence count. We begin with a measurement: on sparse temporal graphs the classical 1-hop common-neighbour signal is near-random (discriminative AUC $\approx 0.50$), because two nodes almost never share a direct neighbour; the genuinely discriminative signal lies one hop deeper -- the 2-hop cohesive bridge, whose discAUC reaches 0.73--0.98, on both bipartite and non-bipartite graphs. Motivated by this, we propose HAWKEYE, a cohesion-aware structural channel that incrementally maintains the classical k-family of cohesiveness indicators (degree $\to$ k-core $\to$ k-truss) and forms 2-hop cohesive-bridge features. HAWKEYE is a drop-in replacement for a temporal-graph model's native structure channel, with no change to the backbone. Swapping HAWKEYE into DyGFormer improves test AP/MRR over the cooccurrence channel by +0.6 to +10.8 points across six multi-seed-validated datasets (uci, enron, USLegis, CanParl, reddit, mooc). On the bipartite recommendation benchmark tgbl-subreddit, a 3-seed single-pass struct-only ablation shows HAWKEYE nearly doubling the baseline test MRR (0.103$\pm$0.003 $\to$ 0.204$\pm$0.005, +10.1 points across all three seeds); the streaming pipeline scales to the 67M-edge tgbl-flight in five minutes per pass. We further characterise when it helps: the gain tracks a graph's training-free 2-hop discAUC and vanishes on degenerate or saturated graphs -- a predictable boundary. All code, data, and figure-generation scripts are released. --- ### SEAM: Shot Entity-Attribute Memory for Consistent Short-Drama Generation at Scale Authors: Jiaqi Liu, Maolin Ran, Xiaoyang Lu, Jian Wang, Weiwen Liu, Jianghao Lin, Yong Yu, Weinan Zhang Published: 2026-08-24 Categories: cs.AI arXiv: https://arxiv.org/abs/2608.22725v1 Short-drama generation has grown into a large, industrialized pipeline, and as it scales from isolated shots to the episode level, visual continuity has become a critical bottleneck. Current agent frameworks generate each shot in isolation, so context drifts across shots and props, character posture, and blocking turn inconsistent. Once assembled, these small discrepancies amplify into severe visual breaks. We present SEAM (Shot Entity-Attribute Memory), a training-free, model-agnostic memory graph that repairs continuity entirely at the prompt-text layer by extracting a multi-dimensional state for every shot, retrieving only causally prior context over the resulting graph, filtering it selectively, and injecting the surviving constraints by natural-language prompt rewriting. We further release SEAM-Bench, a double-blind continuity storyboarding benchmark, on which SEAM raises cross-episode continuity recall from 0.700 to 0.946, generalizes across six mainstream text models, and yields consistent, though not yet significant, gains at the generated-image layer. Deployed as a mandatory stage in CreativeFitting's SEAM-Agent production pipeline over 201 shots, SEAM reaches a 96.5% director-acceptance rate with zero unsafe injections; a conservative counterfactual attributes at least 21.9 percentage points of that rate to its cross-episode memory. --- ### LoViF 2026 The First Challenge on Unified Removal of Raindrops and Reflections: Methods and Results Authors: Zewei He, Xi Tong, Yu Chen, Xingyu Liu, Xin Li, Zepeng Wang, Jiagao Hu, Fuhao Li, Yuxuan Chen, Fei Wang, Daiguo Zhou, Minmin Yi et al. Published: 2026-08-24 Categories: cs.CV arXiv: https://arxiv.org/abs/2608.22723v1 This workshop paper comprehensively reviews the First Challenge on Unified Removal of Raindrops and Reflections. The challenge aims to address a frequently encountered practical problem in the field of autonomous driving, i.e., raindrop-reflection composite degradation on rainy days. This competition attracted 149 registered participants and received 12 valid final submissions with corresponding fact sheets, significantly contributing to the progress of unified removal of raindrops and reflections. All the methods are developed and evaluated on our real-shot RainDrop and ReFlection (RDRF) dataset. A detailed analysis of the submitted methods and corresponding results is provided in this report, which highlights effective approaches and provides interesting insights for future research. --- ### EGAMA-RC: Risk-Calibrated Evidence-Gated Adaptive Malware Analysis for Robust and Interpretable Memory-Forensic Triage Authors: Isaac Kofi Nti Published: 2026-08-24 Categories: cs.CR, cs.LG arXiv: https://arxiv.org/abs/2608.22721v1 Machine-learning malware detectors often achieve high clean-data accuracy, but operational triage also requires evidence about uncertainty, novelty, robustness, interpretability, latency, and review cost. This paper presents EGAMA-RC, a risk-calibrated evidence-gated framework for memory-forensic malware triage. Building on SHAP-guided feature refinement, EGAMA-RC combines dataset-specific refinement, model-pool evaluation, adversarial and open-family testing, novelty scoring, explanation-conditioned evidence, and runtime-aware routing. Low-risk samples are accepted automatically, while uncertain, high-risk, or potentially novel cases are routed to review, escalation, or novelty-aware handling. Across three malware datasets and a frozen multi-seed protocol, the selected hybrid gate accepts 93.12% of pooled samples with 99.86% accepted accuracy and a 0.136% false-accept rate. Novelty calibration reduces over-restrictive review behavior while preserving a low unsafe-accept profile. XGBoost provides lightweight fast-path inference with p50/p95 latency of 0.0054/0.0059 ms per sample. The results show that dependable malware analysis requires risk-calibrated routing, novelty awareness, and controlled analyst review, not classification accuracy alone. --- ### TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge Authors: Kurt M Wilson, Mohaiminul Al Nahian, Abeer Matar A. Almalky, Sadat Shahriyar, Souvik Kundu, Zhishan Guo, Abdullah Al Arafat, Adnan Siraj Rakin Published: 2026-08-24 Categories: cs.CR, cs.AI arXiv: https://arxiv.org/abs/2608.22716v1 Despite their remarkable success, machine learning models, particularly in vision applications, are alarmingly vulnerable to a range of security threats. One key factor in the attack landscape is the distinction between white-box and black-box threat models, as the latter poses challenges that limit attack effectiveness when access to model information is limited. As a result, using Trusted Execution Environments (TEEs) enhances security for machine learning applications by protecting model confidentiality and execution integrity, effectively shifting the execution environment from the white-box to the black-box side of the threat model spectrum. While adopting TEEs for large vision models, e.g., Vision Transformers (ViTs), is crucial for enhancing security and privacy, significant challenges related to memory constraints and increased computational latency must be addressed, especially in time-sensitive edge applications where safety and privacy are paramount. The objective of this work is to enable large vision models to be fully hosted within TEEs, achieving GPU-level inference latency for time-sensitive edge vision applications while maintaining performance. To this end, we propose TEE-X, a TEE-aware acceleration framework that introduces a sensitivity-aware modularization technique and enables vectorization in TEE inference. This design is validated on OP-TEE for Arm TrustZone, configured to optimize performance on the NVIDIA Jetson AGX Xavier for efficient edge vision applications using ViT models. The findings reveal that TEE-X delivers an effective TEE-aware acceleration framework that achieves minimal accuracy-latency trade-offs while ensuring fast and secure edge inference for vision models. --- ### A Source-Grounded Framework for Constructing and Evaluating Progressive Multimodal Diagnostic Dialogues from Clinical Case Reports Authors: Yufan Wang, Rui Yang, Yi Liu, Yi Lin, Yifan Peng Published: 2026-08-24 Categories: cs.CL arXiv: https://arxiv.org/abs/2608.22713v1 Clinical diagnosis requires progressive integration of patient history, physical examination, laboratory findings, medical images, and diagnostic-informative tests. However, most multimodal medical benchmarks evaluate fixed inputs or endpoint answers, while fully interactive diagnostic agents conflate evidence selection with evidence interpretation. We present a source-grounded framework to construct progressive multimodal diagnostic dialogues from case reports and an evaluation strategy for assessing MLLMs on final diagnosis, diagnostic reasoning, and image-finding interpretation. Evaluation on 24 internal medicine case reports showed that our framework can accurately convert case reports into reference dialogues, achieving a diagnosis F1 of 0.99 and a reasoning-quality score of 4.79 out of 5. Evaluation on two frontier MLLMs (o4-mini and Claude Haiku 4.5) achieved reasoning-quality scores of 2.75 and 2.50, respectively, with substantially lower diagnosis, reasoning, and image-finding F1 scores. The results demonstrate that fluent responses do not necessarily reflect evidence-grounded clinical reasoning and highlight the utility of the proposed framework for evaluating multimodal diagnostic reasoning. --- ### CacheRouter: A Dual-Path Tool Routing Architecture with Cache-Preserving Main-Model Isolation for Long-Tail Tool Discovery Authors: Donghui Zha, Lingwei Xu, Linxiao Wu, Yixue Dong, Haochen Li Published: 2026-08-24 Categories: cs.AI arXiv: https://arxiv.org/abs/2608.22708v1 Tool use in LLM systems faces a structural trade-off. Progressive disclosure keeps the prompt small by showing only the tools relevant to the current task, while prompt caching rewards a request prefix that stays fixed across calls; every change to the visible tool list invalidates the cached prefix. This paper treats the trade-off as a problem of request architecture and proposes a dual-path routing design that assigns tool selection and tool delivery to separate channels. The main model always sees a small, fixed set of core tools, so the head of its request is unchanged across calls; all other tools are reached through an independent routing channel, in which a router sub-model searches the full tool list, selects one tool, executes it, and returns the result. Tool registration is automated from source code and supports runtime updates, so the tool set can grow without modifying the main model's request prefix. The design generalizes progressive disclosure: capabilities are disclosed through the routing channel, and the main model's prefix stays stable. A prototype implementation was exercised on 55 functional queries and a 30-turn dialogue; token-level cache hit rates reached 90.99% and 95.2%, cutting input cost to about 12.0% and 8.0% of a no-cache baseline under DeepSeek's pricing, where cache-hit input tokens cost roughly 1/30 of cache-miss tokens. --- ### WnW: Waxing-and-Waning KV Cache for Long-Form Speech LLMs Authors: Yiming Yao, Chenyang Lyu, Xuanfan Ni, Longyue Wang, Weihua Luo, Yazheng Yang, Jinsong Su Published: 2026-08-24 Categories: cs.CL, cs.SD arXiv: https://arxiv.org/abs/2608.22704v2 Long-form audio inputs make the KV cache the dominant memory cost of speech LLMs. Prefill-only KV compression methods permanently discard audio KV positions once evicted, with no pathway to recover them during decoding. We show this is fragile on long-form audio: prefill attention concentrates near the audio start (an attention-sink effect), while decode-time attention distributes broadly, and the two rankings overlap weakly. We propose WnW (Waxing-and-Waning KV cache), which classifies KV-heads into anchor, tidal, and fixed roles via offline calibration. Anchor heads keep all audio KV on GPU and yield a decode-time signal of which audio region each token is read from; tidal heads keep a CPU-resident complement that is recalled chunk-by-chunk based on aggregated anchor-head scores; fixed heads keep only an on-GPU subset, with the rest permanently discarded. On LibriSpeech-Long with two 3B backbones (Voxtral-mini-3b and Qwen2.5-Omni-3B), WnW preserves near-Full-Cache accuracy while keeping only 20% of audio tokens on GPU, where prefill-only baselines fail to terminate. Results generalize across language, task, and domain shifts, and CPU-GPU recall adds little decode-time overhead in our measurements. --- ### Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf Authors: Davood Wadi, Yu Ma Published: 2026-08-24 Categories: cs.AI, econ.GN arXiv: https://arxiv.org/abs/2608.22697v3 Search rankings are valuable because human attention is scarce and sequential. Higher-placed alternatives are easier to find, so they are examined and bought more often. Consumers are now delegating search to AI agents that can ingest an entire results page at once. Randomizing the order of one hundred hotel listings across 5,000 AI agent sessions, we compare four large language models against human field data. AI agents search more deeply than humans and never decline to buy. Position still predicts which listings are inspected, but weakly and non-monotonically: the middle of a results page has the lowest probability of inspection, not the bottom. Position reaches the choice stage for some models and not others, a heterogeneity that tracks neither provider nor capability. All models nonetheless converge on the same undominated listing. For agentic search, the attributes displayed on a results page matter more than placement within it. --- ### Enrich-Retrieve-Rank: Scaling Capability Discovery Beyond In-Context Routing Authors: Nazib Sorathiya, Daniel Zhang, Bardiya Akhbari Published: 2026-08-24 Categories: cs.CL, cs.AI, cs.IR arXiv: https://arxiv.org/abs/2608.22695v1 Agent ecosystems now include thousands of MATS components (Models, Agents, Tools, and Skills), yet their discovery still relies on in-context routing. These systems read a registry (names, hints, or descriptions, as context budget permits), pick a candidate, invoke it, and retry on failure. This pattern degrades with scale, and registries are growing fast. We recast capability discovery as search over a registry by defining an offline enrichment step that turns sparse metadata into searchable profiles, and an online retrieve-then-rank pipeline that returns a ranked shortlist without invoking any candidates online. We show that from N=10 to 7,278 capabilities, in-context routing's top-1 accuracy (Match@1) collapses (0.85 to 0.12), while retrieve-then-rank degrades more gently (0.81 to 0.39) because its reranker still ranks the right capability first 0.70-0.87 of the time once retrieval finds it. In the Nova Micro sweep, the crossover is around N=500. We compare against two in-context baselines. Full-Ctx puts the whole registry in the prompt and asks the LLM to pick. Search&Pick gives the LLM a search tool to narrow candidates before it picks. At full scale the pipeline leads Search&Pick by 6.5 percentage points (pp) on Match@1 at about half the cost. It reduces cost 70x versus Full-Ctx. We use a fixed configuration (same enrichment, retriever, and scorer weights) across agent, tool, and skill registries. The pipeline runs in production as the default capability-discovery layer of a large-scale multi-agent platform. --- ### Hybrid Generative-Discriminative Object Placement Authors: Siyuan Zhou, Li Niu Published: 2026-08-24 Categories: cs.CV arXiv: https://arxiv.org/abs/2608.22692v1 As an important operation of image composition, object placement aims to predict the plausible placement (location, scale) for the inserted foreground object. Previous object placement methods can be divided into generative methods and discriminative methods, both of which cannot balance efficiency and effectiveness well. In this work, we propose a semi-generative method in the middle ground between them. In particular, we assign uniformly distributed anchors on the background. Then, we fuse foreground and background features to predict the rationality score for each anchor and predict plausible placement sets for positive anchors. Extensive experiments on the OPA dataset show that our method can strike a good balance between efficiency and effectiveness. --- ### MorphoCLIP: Text-Supervised Contrastive Learning for Perturbation Matching in Cell Painting Images Authors: Sukhrobbek Ilyosbekov, Shubham Gajjar, Rongfei Jin Published: 2026-08-24 Categories: cs.CV arXiv: https://arxiv.org/abs/2608.22690v1 Cell Painting microscopy captures how cells change after a chemical or genetic perturbation. Connecting these images to the perturbations that produced them could make large imaging screens easier to search and interpret, but the task remains difficult because biological effects are subtle and technical variation is substantial. We introduce MorphoCLIP, a contrastive model that links Cell Painting profiles with text descriptions of compounds, CRISPR knockouts, and ORF overexpressions. The model keeps its vision and language backbones frozen and trains only a compact cross-channel module and projection layers, so it can be trained on a single consumer GPU. On held-out CPJUMP1 data, MorphoCLIP searches in both directions: from a cell image to its perturbation description and from a description to matching cell images. In both cases, a correct match appears among the top ten results much more often than expected by chance. Adding a replicate-alignment loss makes profiles from repeated experiments more consistent, although this improvement does not yet translate into reliable gene-compound matching. Gene-aware labels and plate correction also show no consistent retrieval benefit. These findings suggest that text supervision can help organize chemical and genetic Cell Painting data. Matching compounds with genetic perturbations, however, remains an open problem. --- ### Maximum-distance nonnegative matrix factorization for unmixing highly mixed grain-size distribution data: A generalization of AnalySize Authors: Qianqian Qi, Zhongming Chen, Peter G. M. van der Heijden Published: 2026-08-24 Categories: cs.LG arXiv: https://arxiv.org/abs/2608.22681v1 Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two nonnegative matrices. This property makes NMF well suited for unmixing grain-size distribution data, which are inherently nonnegative and have row sums equal to one. Previous studies have shown that AnalySize, an NMF-based method, performs well on poorly mixed grain-size distribution data but struggles when the data is highly mixed, where no observed samples are close to the true end members. To overcome this limitation, we introduce a maximum-distance NMF that encourages the estimated end members to be as distinct as possible and develop a hierarchical alternating least squares algorithm for optimization. The proposed formulation can be regarded as a generalization of AnalySize, where AnalySize minimizes the distance among end members while the proposed method maximizes it. Experimental results demonstrate that the method effectively decomposes highly mixed grain-size distribution data. --- ### Contextrast++: Robust Multi-Scale Contextual Contrastive Learning for Semantic Segmentation Authors: Changki Sung, Hyungtae Lim, Wanhee Kim, Youngwoo Seo, Hyun Myung Published: 2026-08-24 Categories: cs.CV, cs.RO arXiv: https://arxiv.org/abs/2608.22679v1 Semantic segmentation has rapidly advanced with deep learning; however, challenges remain in effectively capturing local and global contexts as well as addressing the long-tailed distribution problem. To tackle these issues, we present Contextrast++, a robust contrastive learning method for semantic segmentation that improves multi-scale feature integration and mitigates class imbalance issues. Our method consists of two key components: 1) contextual contrastive learning (CCL) and 2) boundary-aware negative (BANE) sampling. CCL includes three subcomponents: adaptive fusion module, pixel-to-anchor (PA) loss, and anchor-to-anchor (AA) loss. The adaptive fusion module dynamically balances local and global feature integration, resulting in a more context-aware representation. While the PA loss leverages the fused multi-scale features to improve feature representation learning, the AA loss focuses on addressing the long-tailed distribution problem by utilizing a memory bank that stores a fixed number of class-balanced representative anchors. Meanwhile, BANE sampling enhances segmentation precision by selecting hard negatives from misclassified boundary regions, which refines fine-grained details during contrastive learning. As verified in extensive experiments using public datasets, we demonstrate that Contextrast++ substantially improves semantic segmentation performance over existing contrastive learning-based state-of-the-art approaches, while introducing no additional computational overhead during inference. --- ### RACO: Reliability-Aware Coarse-Goal Optimization for Inspection-Oriented UAV Vision-Language Navigation Authors: Sen Wang, Yiming Sun, Jiaxuan He, Pengfei Zhu Published: 2026-08-24 Categories: cs.RO, cs.AI, cs.CV arXiv: https://arxiv.org/abs/2608.22678v1 UAV vision-language navigation (UAV-VLN) is commonly evaluated as goal reaching, but inspection-oriented deployment requires the agent to stop within a valid inspection region and avoid falsely confirming visually or semantically similar distractors. This requirement exposes a key weakness in existing coarse-to-fine UAV-VLN policies: the coarse goal predicted before local refinement is often treated as reliable, although it may drift toward plausible but incorrect object regions and limit the ability of the local stage to recover. To systematically evaluate this problem, we introduce LG-UVI, an object-centric inspection evaluation setting derived from CityNav/CityRefer. LG-UVI extends standard UAV-VLN episodes with target objects, hard distractors, type-aware inspection regions, and diagnostics for inspection-region arrival and object-level confirmation. To address this inspection-oriented setting, we further propose RACO, a reliability-aware adaptive coarse-to-fine navigation framework. Instead of treating the predicted coarse goal as a fixed waypoint, RACO views it as a runtime hypothesis and uses object-level candidate anchors to check and correct coarse localization before Stage 1 and at the Stage 1-to-Stage 2 boundary. RACO also applies scale-adaptive terminal refinement to handle terminal near-miss cases using runtime-observable geometric and anchor-based evidence. Under a unified online evaluation protocol, RACO improves SR over the reproduced HETT baseline by 9.53 and 7.98 percentage points on validation-unseen and test-unseen, respectively. It also improves inspection-region arrival and reduces false verification risk, showing that coarse-goal reliability optimization is an effective complement to existing coarse-to-fine UAV-VLN policies. --- ### Robustness Analysis of Agentic AI to Inconsistent and Incomplete Tool Responses Authors: Jiachen Xu, Torben Bach Pedersen, Zhongming Yao, Xiaoyu Zhang, Yushuai Li Published: 2026-08-24 Categories: cs.AI arXiv: https://arxiv.org/abs/2608.22676v2 Tool-using agents increasingly rely on external tools to complete multi-step tasks, but tool returns can fail in different ways and require different recovery actions. Existing robustness studies often use uncertainty-based measures to detect when an agent becomes unreliable. These measures can reveal that something has gone wrong, but they do not directly identify the type of tool failure or the appropriate response. We address this limitation by analyzing tool failures at the moment a return enters the agent context. Our approach combines two complementary signals. The first compares the likelihood of the returned content under the tool schema and under the full trajectory prefix. The second measures the agent's probability distribution over its legal next actions. We evaluate the approach by injecting incomplete and inconsistent returns into a retail customer-service benchmark. The results show that likelihood-based signals clearly capture incomplete returns and some direct inconsistencies, while action-based signals reveal how strongly a failure changes the next decision. Some failures that are weak under likelihood signals can still redirect the agent toward state-changing actions. These findings show that tool failures can be recognized at the return boundary, but reliable diagnosis requires combining multiple signals. --- ### A-CPES: A Reference Framework for Agentic AI in Cyber-Physical Energy Systems Authors: Xiaoyu Zhang, Qiuye Sun, Jiachen Xu, Zhongming Yao, Yushuai Li Published: 2026-08-24 Categories: cs.AI arXiv: https://arxiv.org/abs/2608.22672v1 Energy system operation contains a loop of work that automation has never taken over: posing the optimization problem the current cycle should solve, disposing of infeasibility, sequencing a solution into interlocked switching orders, assembling evidence no single model holds, negotiating adjustable capacity with many parties, and settling experience into practice. Licensed dispatchers carry all of it in person, and the rising share of variable renewable generation is making that loop turn faster than their number can grow. Agentic AI supplies the abilities it requires, but enters as the outer loop of control: it calls SCED and the other decision models rather than being called by them. We propose A-CPES, three nested rings, an authorization and accountability frame around an agentic control outer loop around a six-layer CPES core. We argue the loop is indivisible, tune where and how tightly it may close, state eight structural failure modes as falsifiable predictions, and specify six governance modules that rebuild the authorization frame until it covers the loop, before the loop starts turning. --- ### Hyperbolic Hierarchical Clustering for Visual Representation Learning Authors: Jianan Wei, Guikun Chen, Zhiyuan Weng, Chunchao Guo, Yujia Wang, Wenguan Wang Published: 2026-08-24 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2608.22665v1 We investigate the token mixer in vision backbones by revisiting clustering, one of the most classic approaches in machine learning. An effective token mixer is a fundamental component of modern vision backbones like vision Transformers, facilitating information exchange between image patches. Mainstream token mixers, which rely on convolution, attention, MLP, or their hybrids, primarily focus on navigating the trade-off between accuracy and computational cost. However, a significant drawback of these methods is their black-box nature; their encoding process is opaque and lacks interpretability. Diverging from these opaque designs, we introduce ClusterMixer, a transparent token mixer that is grounded in a clustering paradigm and interpretable by design. ClusterMixer explicitly formulates the token mixing process through a hierarchical clustering mechanism. To model the natural, tree-like relationships inherent in visual data, the clustering is performed in hyperbolic space, which is well-suited for embedding hierarchies with low distortion. Building on this innovation, we present HCFormer, a new backbone architecture that integrates ClusterMixer with a series of meticulously designed clustering strategies to ensure robust performance across tasks. Extensive experiments demonstrate that HCFormer consistently outperforms its counterparts across diverse tasks, including image classification, object detection, instance segmentation, and semantic segmentation. Considering its transparency and efficacy, we hope HCFormer can facilitate a paradigm shift toward interpretable backbones. --- ### Linear RNN Scaling Laws: When Longer Sequences Beat More Sequences Authors: Ziyan Chen, Zhongzhu Zhou, Peilin Liu, Ding-Xuan Zhou Published: 2026-08-23 Categories: cs.LG arXiv: https://arxiv.org/abs/2609.27964v1 Empirical scaling laws for autoregressive language models relate prediction loss to model size, data size, and optimization compute, but their theoretical origin is still poorly understood in sequential pretraining settings. We study this question in a tractable teacher--student model where a stable latent linear RNN generates trajectories and a sketched linear recurrent student is trained by safeguarded full-batch WSD gradient descent on next-token prediction. The sketch dimension $M$ plays the role of model size, while $N$ independent trajectories of length $P$ provide the training tokens. We allow the innovation and initialization covariances to have different power-law exponents $α$ and $θ$. The induced design spectrum produces explicit approximation, optimization, and statistical scaling laws separated by spectral crossovers. When $θ\geα$, the original one-scale rates $M^{1-β_α}$, $R^{(1-β_α)/α}$, and $(NP)^{-1}\min\{M,R^{1/α}\}$ are recovered. When $α-2r\leθ<α$, the heavier initialization tail changes the rates beyond $P$-dependent model and optimization crossovers. The proof uses a covariance event only internally and a globally safeguarded step size on its complement. The variance retains the factor $(NP)^{-1}$, while sequence length also suppresses the initialization transient, so $N$ and $P$ cease to be fully interchangeable in the two-scale regime. --- ### I-SplineFlow: Learning Monotone Spline Stochastic Interpolant Schedulers for Few-Step Generation Authors: Md Sakib Hossain Shovon, Md Rifat Ur Rahman, Md Abtahi Majeed Chowdhury, Yunhong Min, Jaesik Choi, Minhyuk Sung Published: 2026-08-23 Categories: cs.LG arXiv: https://arxiv.org/abs/2609.27963v1 Few-step generation with pretrained diffusion and flow models can be accelerated by lightweight training that optimizes the sampling trajectory rather than the network. A recent approach parameterizes the stochastic interpolant (SI) scheduler as a smooth curve whose control points enforce the three properties an SI scheduler must satisfy: fixed boundary conditions, a monotone signal-to-noise ratio (SNR), and differentiability. Existing parameterizations use globally supported polynomial bases, where every control point moves the whole curve and higher expressiveness needs a higher degree, which couples distant regions of the schedule during optimization. We introduce \emph{I-SplineFlow}, which parameterizes the scheduler with integrated monotone splines (I-splines). I-splines decouple the polynomial degree from the number of mixture weights, so support width and smoothness can be chosen per model at a fixed weight count, and the compactly supported derivative basis makes the scheduler Jacobian orders of magnitude better conditioned than a Bézier basis. Boundary conditions and a strictly monotone SNR hold by construction, with no ordering constraint on the parameters and closed-form velocity derivatives. Across diffusion (EDM) and flow (ReFlow, Simple ReFlow) models, I-SplineFlow improves few-step FID over Bézier scheduling in most settings, most clearly at the lowest NFEs, and trains in minutes. Ablations show that both the degree freedom and the monotonicity constraint are needed. The code will be released upon acceptance. --- ### Local SVD-Entropy Maps as a Complementary Structural Representation for Full-Reference and No-Reference Image Quality Assessment Authors: Andrei Velichko, Petr Boriskov Published: 2026-08-23 Categories: eess.IV, cs.CV, cs.MM arXiv: https://arxiv.org/abs/2609.27959v1 We investigate a local spectral-complexity representation for perceptual image quality assessment (IQA) based on Shannon entropy of singular values computed directly from two-dimensional image patches. For each $3\times3$-pixel grayscale patch, SVD is applied directly and the normalized singular-value entropy defines one HSVD-map value. The construction requires neither flattening nor delay embedding, uses no boundary padding, and is invariant to $90^{\circ}$ rotations and mirror reflections at the local-descriptor level. A nested salt-and-pepper experiment on Lena separates absolute similarity to a clean reference from sensitivity to an additional degradation step. HSVD-SSIM responds more strongly to local corruption and retains a larger neighboring-state response at severe noise levels. Validation on all 10,125 distorted KADID-10k images shows that HSVD-SSIM is weaker than conventional SSIM as a standalone full-reference metric (SRCC $0.450$ vs. $0.619$), but complementary when combined with it: grouped cross-validation increases SRCC from $0.618$ to $0.659$, with a bootstrap 95\% confidence interval of $(0.036,0.046)$ for the gain. In a no-reference experiment, adding HSVD-derived single-image descriptors improves the best nonlinear model from SRCC $0.528$ to $0.575$ (95\% CI $(0.033,0.061)$) and also improves prediction of quality changes between neighboring distortion states. These results support direct local SVD entropy as an interpretable structural channel that complements conventional image-domain similarity and remains informative without a pristine reference. --- ### CS-WCP: Robust Conformal Sets for LLM-Judge Traffic Shifts with Uncertain Group Proportions Authors: Ibne Farabi Shihab, Fariya Afrin Published: 2026-08-23 Categories: cs.LG arXiv: https://arxiv.org/abs/2609.27955v1 Prediction sets built from an LLM judge can undercover when deployment traffic changes the prevalence of task or policy groups. Weighted conformal prediction is exact under covariate shift when the density ratio is known, but group proportions must usually be estimated from finite unlabeled samples. We introduce confidence-set weighted conformal prediction (CS-WCP), which constructs simultaneous exact intervals for source and target group masses and returns the union of weighted conformal sets over every compatible ratio vector. For a fixed or independently learned finite partition, CS-WCP attains coverage at least 1-alpha-delta_w-tau_A-kappa, where tau_A measures within-cell covariate mismatch and kappa measures conditional shift. A linear endpoint rule computes the robust union in O(G|Y|) time. Across 336 constructed shared-support traffic shifts, CS-WCP reaches 0.973 mean coverage with 13 point failures, compared with 0.954 and 44 failures for source conformal prediction, at mean binary set sizes 1.74 and 1.65. On 336 natural cross-task transfers, coverage rises from 0.882 to 0.962, but mean set size reaches 1.87 and a size-matched group plug-in baseline is competitive. The method therefore supplies an auditable coverage safeguard under uncertain mixture weights; its value is conservative tail protection, not scalar probability calibration or uniformly smaller sets. --- ### When Accuracy Gaps Fail to Certify: Auditing Cross-Domain Recalibration of LLM Judges Authors: Fariya Afrin, Ibne Farabi Shihab Published: 2026-08-23 Categories: cs.LG arXiv: https://arxiv.org/abs/2609.27954v1 A scalar recalibration map fitted for an LLM judge on one task can fail when the task distribution changes, but the source-target accuracy gap is often treated as a proxy for that failure. We test what this gap can predict and what it can certify across thirteen judges, two generators, eight domains, and 1,176 predeclared transfers. After accounting for mean score shift, the gap yields a population lower bound on target calibration error, yet identical gaps can induce opposite transfer outcomes. Exact importance weighting recovers target proper loss under covariate shift, so failure of an estimated weighting pipeline does not by itself establish conditional shift. A finite-sample simultaneous lower certificate converts the population bound into a one-sided rejection rule using audit labels disjoint from evaluation outcomes. The leak-free gap correlation is 0.25 (95% CI (-0.09, 0.55)), falls to 0.09 on the second generator, and does not support a generator-invariant association. The certificate retains nominal coverage but has power 0.13 even at m=1024, whereas target-domain temperature scaling with 16 labels reaches harm rate 0.09, compared with 0.34 for source-fitted Platt scaling. Accuracy gaps are therefore weak warning signals for scalar probability transfer, not deployment certificates. --- ### Enhancing Multiclass Malware Classification in Resource-Constrained Environments Authors: Abdul Khalek Alve, Alif Rahman, Saadman Zaman, Sazzad Hossen Himel, Muhammad Iqbal Hossain Published: 2026-08-23 Categories: cs.CR, cs.LG arXiv: https://arxiv.org/abs/2609.27950v1 The emergence of multi-class malware attacks such as ransomware, spyware, trojans, etc., presents an increasing and serious threat to cybersecurity, particularly in resourceconstrained environments like IoT devices. Existing machine learning models have achieved nearly perfect accuracy in binary malware classification but fall short in terms of classifying malware families and individual malware. Additionally, the complexity of these multi-class malware attacks presents a significant challenge of detection in resource-constrained environments, as multi-class detection usually requires high computational capability. This research bridges the gap by enhancing the detection accuracy of multi-class malware classification as well as developing a lightweight model that can run efficiently on resource-constrained devices. In this paper, we propose a robust, lightweight machine learning model featuring LightGBM classifier with SMOTE oversampling and SOM-US undersampling techniques for data balancing, as well as well-engineered feature selection through Genetic Algorithm. The model performed better than the current state-of-the-art models developed on the same dataset in both malware family classification (4 classes) and individual malware type classification (16 classes) with accuracy of 89.1% and 76% respectively. Thus, maintaining a balance between classification accuracy and computational efficiency in resource-constrained environments. Furthermore, we propose another model using Random Forest classifier with an accuracy of 91.2% in malware family classification and 78.7% in individual malware classification. Demonstrating a significant enhancement in terms of accuracy from the current state-of-the-art models. --- ### From Sentiment Classification to Actionable and Responsible Feedback: A Scoping Review and Evidence Map of NLP in Student Evaluation of Teaching, 2015-2026 Authors: Jeff Eicher, Rafael da Silva Published: 2026-08-23 Categories: cs.CL, cs.LG arXiv: https://arxiv.org/abs/2609.27939v1 Natural language processing (NLP) applied to open-ended teaching-evaluation comments (Student Evaluation of Teaching, SET) has tracked the field's technical evolution--from lexicons and conventional classifiers to transformers and large language models (LLMs)--but it is not evident that this technical diversification has been accompanied by corresponding gains in educational value and robustness of the evidence. This scoping review (PRISMA-ScR) maps 421 studies (2015-2026, 2026 partial) along a technical axis (RQ1) and four value dimensions (RQ2-RQ5). Dual mutually blinded LLM screening with sampled human adjudication coded seven extraction domains, with targeted codebook-boundary review at synthesis. The joint map's sharpest quantified gap is the actionability discontinuity: demonstrated output or stronger (A2+: 258/421; 61.3%) versus intended-user evaluation or stronger (A3+: 49/421; 11.6%), a 49.7 percentage-point drop. Sentiment analysis remains the modal task (300/421); diagnostic and generative depth is a substantial minority (D4-D5: 28.2% of resolved cases); a formal fairness metric is rare (1.9%). The findings are descriptive and do not support causal claims of progress: technological coexistence and uneven reporting are part of the map, but the A2+ to A3+ cliff is the contribution, not a quality ladder. --- ### Block-Level Weight-Space Structure Persists Under Post-Training: An Empirical Study Across LLM Families Authors: Zhaohui Wang Published: 2026-08-23 Categories: cs.LG arXiv: https://arxiv.org/abs/2609.26147v1 Modern LLMs are deployed as families of post-trained variants (base, instruct, chat, code) derived from a shared set of pre-trained weights. We present an empirical study of how post-training transforms weight-space geometry, covering eight configurations across four architecture families (Qwen2.5, Llama-3.1/3.2, Mistral, Gemma-2). We identify a granularity gap: post-training modifies every tensor (zero of 291-339 tensors remain byte-identical, so hash-based deduplication achieves 0% savings), yet preserves block-level structure (mean cosine similarity exceeds 0.99 and relative Frobenius distance stays below 0.13). Post-training therefore acts as a structured perturbation that shifts every parameter while leaving block-level geometry intact. The property is not universal: independently trained specializations (for example, Qwen2.5-Coder) attain cosine similarity around 0.64 with the general base, indicating a disconnected region of weight space. Perturbation magnitude varies systematically with model scale, architecture family, and post-training recipe. As a practical application, we build LinkerLLM, a lazy loader that aliases shareable blocks across co-resident variants, achieving 18-48% GPU memory savings and enabling up to five 7B-parameter variants on a single 24 GB consumer GPU. Five of eight configurations retain at least 94% of the unshared variant's quality on MMLU, ARC-Challenge, HellaSwag, and WinoGrande; the remaining three (Mistral-7B, Gemma-2-2B, Llama-3.2-1B) have one below-threshold benchmark each (87-91%), which we report transparently rather than gate the block-sharing decision on a single threshold. --- ### Correlation-Aware Structured Pruning for Large Language Models Authors: Sicheng Xu, Hao Shi, Wei Zhang, Haoran Pang, Zhenyu Ming, Hao Wu, Zhongyi Huang, Xin Yao, Gong Zhang Published: 2026-08-23 Categories: cs.CL, cs.LG arXiv: https://arxiv.org/abs/2609.22131v1 Structured pruning is a promising approach for reducing the substantial inference costs of Large Language Models (LLMs) while maintaining hardware efficiency. Many existing methods assess the importance of prunable units (e.g., channels or heads) in isolation, implicitly assuming that pruning errors are additive. This independence assumption is often invalidated by the non-orthogonality of model weights and strong correlations between unit activations, potentially leading to performance degradation. To address this, we propose a Correlation-Aware Structured Pruning method. We formulate the pruning objective as a cardinality-constrained binary quadratic program that explicitly models cross-unit dependencies in the reconstruction error. Since this binary quadratic program is NP-hard and difficult to solve exactly, we develop a greedy interaction algorithm based on dependency-aware marginal costs to optimize unit selection. Furthermore, we incorporate a gradient-based strategy to achieve adaptive layer-wise sparsity allocation across the entire model. Extensive experiments on mainstream LLMs demonstrate that incorporating correlation information yields competitive accuracy-efficiency trade-offs compared to representative structured pruning baselines. --- ### Hierarchical Bayesian optimization of an aircraft-based multi-agent system-of-systems Authors: Paul Saves, Thierry Lefebvre, Nathalie Bartoli, Jasper Bussemaker, Nikolaos Kalliatakis, Nabih Naeem, Prajwal Prakasha Published: 2026-08-23 Categories: cs.LG, cs.MA, math.OC arXiv: https://arxiv.org/abs/2609.22130v1 Developing innovative system architectures increasingly relies on advanced modeling and optimization techniques to frame the architecting process and define the corresponding computational problems. For complex System-of-Systems (SoS), high-fidelity multiphysics and multidisciplinary simulations are essential for capturing detailed behaviors. However, their computational expense and the risk of evaluation failures make direct optimization challenging. To overcome these limitations, surrogate-based approaches, like Bayesian optimization, have emerged as effective tools for managing expensive, black-box simulation tasks. This work introduces a hierarchical Bayesian optimization framework that leverages Gaussian process meta-modeling to handle discrete architectural choices, conditional dependencies, and heterogeneous design variables inherent to SoS problems. Results show that the hierarchical formulation improves search efficiency and robustness compared to conventional surrogate-based methods, enabling the exploration of large and structurally diverse design spaces with limited simulation budgets. We apply the approach to an aircraft-based multi-agent system for wildfire suppression, a use case developed within the EU-funded COLOSSUS project that illustrates how SoS principles can coordinate heterogeneous aerial platforms with complementary roles, supporting both sustainable mobility and emergency response missions. Our framework provides a scalable methodology for SoS architecting and model exploration, offering transferable insights for applications in aviation, sustainable mobility, and resilience-oriented system design. By combining hierarchical representations with surrogate-based optimization, this work is among the first practical demonstrations of hierarchical Bayesian optimization applied to real-world SoS problems, advancing both methodology and practice. --- ### Helix-FNO: Spectral-Domain Operator Learning Coupled with a High-Fidelity Mechanistic Model for Fast Surrogate Simulation Authors: Jiabao Zhao, Chuwei Wang, Jinxi Yang Published: 2026-08-23 Categories: cs.LG arXiv: https://arxiv.org/abs/2609.22129v1 Mechanistic simulation models of full-scale treatment processes remain the only trustworthy, extrapolative description of the underlying physico-chemical dynamics, yet their runtime is far too slow to support the thousands of forward evaluations that a modern decision engine requires at a 5-minute decision cadence. The standard remedy-surrogate modelling-often produces a network that learns a single solution for a single configuration, so it generalises poorly to new influent profiles, control settings or plant layouts. This paper presents Helix-FNO, a teacher-student architecture that couples a thirty-two-state mechanistic teacher with a Fourier neural operator (FNO) student. The teacher supplies a high-fidelity dataset of input-field-to-solution pairs, curated by Latin-hypercube and uncertainty-based active learning to cover the boundary and overload regimes that matter in practice; the student learns, in the spectral domain, the solution operator itself rather than any single solution, thereby moving from learning one instance to learning an entire family of equations. We give the operator formulation, the spectral convolution definition, the weighted distillation loss and the active-learning criterion, and we analyse the approximation error of a truncated Fourier expansion with respect to the smoothness of the parametric solution manifold. An illustrative study compares Helix-FNO against a physics-informed network and a data-driven recurrent surrogate on accuracy, dataset efficiency and inference latency, and places the methods on a speed-accuracy Pareto front. The resulting operator is three orders of magnitude faster than the mechanistic teacher at millisecond inference, which is precisely the capability required for massive candidate screening and online decision support. --- ### ST-Topo GAN: A Motor EEG-to-EMG Decoding Model Matched to Wrist Movement Complexity Authors: Ye Sun, Mingxuan Qu, Jing Wang, Dezhong Yao, Gang Liu Published: 2026-08-23 Categories: eess.SY, cs.LG arXiv: https://arxiv.org/abs/2609.22128v1 The wrist plays a critical role in upper-limb function by enabling precise hand positioning, force regulation, and object manipulation. Continuous brain--muscle interfaces (BMIs) offer a promising approach for motor restoration by decoding neural activity into muscle activation signals. However, existing EEG-to-EMG models have mainly been developed for tasks with relatively stable muscle synergies and may be less effective for the heterogeneous and weakly coupled neuromuscular organisation involved in wrist movements. This paper proposes ST-Topo GAN, a Spatial--Temporal Topological Generative Adversarial Network for continuous EEG-to-EMG decoding of wrist movements. The framework integrates multi-band EEG representation, sensorimotor cortical topology modelling, and conditional adversarial learning to reconstruct multi-channel iEMG activation. The model was evaluated through cross-task comparison, wrist EEG-to-iEMG decoding, and ablation experiments. Compared with the WAY-EEG-GAL grasp-and-lift dataset, the wrist dataset exhibited lower inter-muscle activation similarity and greater decoding difficulty for conventional models. ST-Topo GAN achieved an average PCC of 0.4436 on the wrist dataset, outperforming all evaluated baselines, while the ablation study confirmed the contribution of its key components. These results support the effectiveness of ST-Topo GAN for continuous wrist EEG-to-iEMG decoding. --- ### Architectural and Regularization Components in Deep Learning Medical Image Registration: Systematic Ablation Study Authors: Nabira Rashid Published: 2026-08-23 Categories: cs.CV, cs.LG arXiv: https://arxiv.org/abs/2609.05484v1 Deep learning registration methods routinely stack two kinds of enhancement on a base network: architectural additions such as affine pre-alignment stages, and training-objective additions such as regularization losses. Papers tend to adopt both at once, so it is unclear which is doing the work. I ran a controlled ablation to separate them. Using the OASIS brain MRI dataset (394 training subjects, 20 test subjects), I trained four variants of the same registration pipeline: a baseline 3D U-Net with basic similarity losses, the same U-Net with a full regularization suite, an affine-plus-deformable architecture with basic losses, and the affine architecture with the full suite. I evaluated registration accuracy (MSE, NCC, SSIM), deformation quality (Jacobian determinant preservation, displacement statistics, an anatomical plausibility score), and computational cost. Regularization alone accounted for most of the gain: a 21.3% relative gain on the MSE-improvement metric (1.78% to 2.16%, P<.001) and a 21.8% relative gain in NCC improvement, while cutting maximum deformation from 53.1 to 0.51 units, a 99.0% reduction, at essentially no computational cost (-0.06% inference time). The combined model produced the largest accuracy gain, 25.8% (1.78% to 2.24%), and raised anatomical plausibility from 0.596 to 0.930, at a moderate +9.8% inference-time cost. Gradient correlation rose from 0.742 at baseline to 0.980 for the fully enhanced model. All enhanced variants reached sub-voxel accuracy under plausible deformation constraints. Regularization losses are the primary driver in this setting, delivering the accuracy gains and almost all of the deformation control for free at inference time, while the affine architecture adds a smaller complementary benefit at acceptable cost. The 99% reduction in unrealistic deformations addresses a known barrier to clinical deployment. --- ### A Heterogeneous General Model for Neuromorphic-Inspired Computation Authors: Matteo Mirigliano Published: 2026-08-23 Categories: cond-mat.dis-nn, cs.NE arXiv: https://arxiv.org/abs/2609.05482v1 In recent years, both academia and industry have focused on the development of computational architectures inspired by the distributed, adaptive, and event-driven characteristics of biological neural systems, with the aim of reducing the computational cost associated with conventional training approaches (1). However, a major challenge is the lack of general models and design guidelines for emerging computational systems and hardware. This work introduces a general model based on an input-dependent stochastic weight network, referred to as a substrate. The substrate weights evolve through input-triggered stochastic updates, with correlations between weight coefficients described by a matrix-valued covariance kernel. The proposed framework is implemented using quadratic polynomial weight functions, where the input amplitude controls the magnitude of the stochastic perturbation and a substrate-dependent distance determines the correlation structure. Numerical simulations show that correlations in the stochastic weight evolution significantly affect the system response, suggesting a potential mechanism for neuromorphic-inspired computation without conventional weight training. The aim of this work is to provide a general formulation of the model and identify its main properties and characteristics. 1 H. Jaeger, Towards a generalized theory comprising digital, neuromorphic and unconventional computing, Neuromorphic Comput. Eng., vol. 1, no. 1, p. 012002, Sep. 2021 --- ### RAPID: Reliability-Aware Pair Importance Distillation Authors: Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi, Omid Kashefi Published: 2026-08-23 Categories: cs.AI arXiv: https://arxiv.org/abs/2609.05481v1 Inter example relational distillation transfers a teacher's representation geometry by matching relations among examples within a mini batch. Computing all pairs has quadratic complexity in the batch size, whereas uniform subsampling may use a limited relation budget inefficiently. We introduce Reliability Aware Pair Importance Distillation, or RAPID, which separates a reliability gated relational target from a full support adaptive pair proposal. Reliability determines which teacher relations are emphasized, while calibrated teacher entropy and detached student-teacher residuals determine which relations are evaluated. Exact inverse proposal correction makes the loss and gradient estimators conditionally unbiased with respect to the gated mini batch target. We evaluate RAPID in two text classification settings: AG News with BERT-to-DistilBERT distillation using three paired seeds and a relation budget of 256, and SST-2 with DistilBERT to DistilBERT distillation using three paired seeds and a relation budget of 64. Reliability gated relational distillation achieves the highest observed mean student accuracy on both datasets: 94.285 plus or minus 0.054 percent on AG News and 88.800 plus or minus 0.532 percent on SST-2. RAPID ranks second, achieving 94.241 plus or minus 0.025 percent and 88.685 plus or minus 0.462 percent, respectively, compared with 94.154 plus or minus 0.124 percent and 87.271 plus or minus 0.162 percent for the cross entropy baseline. Pilot evaluations are counted toward the same total budget as the main relation evaluations. Across both settings, the gated target yields the highest mean accuracy, while the adaptive proposal remains within seed-level variation. These results support the modular view that target reliability and evaluation priority are separable design dimensions. --- ### Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting Authors: Sadegh Mohammadian Published: 2026-08-23 Categories: cs.CV arXiv: https://arxiv.org/abs/2608.28666v2 Video-language models (VLMs) remain brittle on tasks that require tracking events over time and grounding answers in specific spatial regions. We propose that part of this limitation can be addressed through better organization of visual evidence at inference time. We introduce structured video prompting, a training-free inference-time method that augments the input video with lightweight spatial structure and temporal structure, providing explicit anchors for organizing evidence across space and time without changing model weights or decoding and without altering the question prompt in the main comparison. We evaluate this approach on two complementary video benchmarks and two open video-language models. Across these settings, structured inputs improve performance in several cases, with gains varying by model and task. Our findings suggest that some failures of VLMs arise not only from reasoning capacity, but also from how video evidence is presented at inference time. These results highlight structured video prompting as a simple and practical direction for improving video understanding. --- ### Understanding Temporal Semantic Stability in Open-Vocabulary UAV Perception through Metric 3D Fusion Authors: Saurbh Singh Jamwal Published: 2026-08-23 Categories: cs.CV, cs.RO arXiv: https://arxiv.org/abs/2608.28665v1 Recent open-vocabulary segmentation models have advanced semantic perception for UAVs, but predictions from moving aerial platforms can remain temporally inconsistent across repeated observations of the same physical scene. We investigate temporal semantic stability by associating frame-wise predictions with persistent world-space locations through metric 3D fusion. We introduce a voxel-level evaluation framework that jointly characterises final semantic agreement, Semantic Belief Drift (SBD), Observation Persistence (OP), and semantic uncertainty. Experiments on UAVid-3D reveal substantial frame-wise semantic flicker and show that high aggregate world-space agreement can overstate temporal stability when locations have limited repeated-observation support. Persistence-stratified analysis shows that recurrent voxels expose greater semantic disagreement, while belief drift decreases as additional evidence accumulates. This behaviour is observed across two segmentation backbones and remains consistent under variations in voxel resolution, geometric association, and temporal sampling density. Conditions that reduce world-space recurrence can increase apparent aggregate stability, demonstrating that semantic consistency must be interpreted together with observation support. Our findings highlight observation persistence as an essential conditioning variable for evaluating long-horizon semantic reliability. --- ### The Potential of Haptic Foundation Models Authors: Jianquan Wang, Haiwei Dong, Abdulmotaleb El Saddik Published: 2026-08-23 Categories: cs.RO, cs.CV, cs.MM arXiv: https://arxiv.org/abs/2608.28664v1 Despite the success of foundation models in language and vision, their expansion into embodied AI is bottlenecked by a lack of generalized touch sensing. This limitation is especially relevant to consumer electronics, where smartphones, wearables, VR controllers, home robots, and health monitoring devices require safe and adaptive physical interaction. Constrained by hardware heterogeneity and the necessity of active physical data collection, current haptic models remain rigidly task-specific. To overcome these limitations, this article explores the transformative potential and developmental trajectory of Haptic Foundation Models (HFMs). We detail the paradigm shift required to transition from passive Large Language Models and Vision Language Models into active HFMs across four core dimensions: action coupling, physical dynamical representation space, continuous time-series data granularity, and action-conditioned future state prediction. Furthermore, we synthesize existing large-scale tactile datasets and benchmark UniTouch, AnyTouch, T3, and Sparsh on TacBench for force estimation, slip detection, and relative pose estimation. --- ### XHotpotQA: A Benchmark for Cross-Lingual Knowledge Composition in Multi-Hop Question Answering Authors: Iman Barati, Arash Ghafouri, Behrouz Minaei-Bidgoli Published: 2026-08-23 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2608.27481v2 Knowledge-intensive multi-hop question answering requires systems to select evidence and compose dependent facts, yet multilingual benchmarks usually translate an entire example into one language. This hides failures at language boundaries inside the reasoning chain. We introduce XHotpotQA, a controlled benchmark for cross-lingual knowledge composition over mixed-language evidence. Each instance is modeled as an evidence-dependency graph whose question, bridge evidence, answer-bearing evidence, and distractors have explicit language assignments. The audited resource contains 15,661 training and 7,405 validation instances, with sentence-level support supervision and supplied distractors. In validation, 99.81% of items cross the question-to-gold-evidence language interface and 95.60% use gold paragraphs in different languages. Across three reader artifacts, full question-evidence mismatch is associated with 10.25 to 15.79 lower Unicode-aware answer F1 than partial alignment, and different-script evidence with deficits of 11.98 to 23.70 points; the corresponding adapted-selector contrasts are 1.71 and 1.78 points. Under this supplied-candidate design, the evaluated readers therefore show substantially larger condition-associated deficits than the selector. XHotpotQA provides role-aware diagnostics, modular evaluation, and an audited test bed for knowledge-based systems that must integrate evidence across languages. --- ### Predicting Consequences and Reinforcing Navigation Policies with Latent World Models Authors: Zengmao Wang, Wei Gao, Shuhan Shen Published: 2026-08-23 Categories: cs.AI arXiv: https://arxiv.org/abs/2608.26190v1 World models enable agents to reason about future outcomes and learn policies from their knowledge of state transition, but existing approaches primarily focus on reconstructing future observations or features, which introduces unnecessary complexity and limits their effectiveness for decision making. In this work, we propose a compatibility prediction Latent World Model (LWM) for robot navigation that predicts action-conditioned latent feature compatibility rather than reconstructing observations. Our key insight is that spatial proximity correlates with latent feature similarity, enabling action consequences to be evaluated directly in latent space. To support counterfactual training, our model leverages action sequences sampled across trajectories and learns to predict which sequences lead closer to the goal. Furthermore, we demonstrate how the learned world model can supervise policy learning from unlabeled video data and further improve policies through reinforcement learning entirely within the world model. This imagination-driven framework eliminates the need for action annotations and additional environment interaction. Extensive experiments on multiple real-world robot navigation datasets show that our approach significantly outperforms prior world model and imitation learning methods in prediction accuracy, policy learning, and real-world navigation performance. The code, pretrained models, and additional materials are available at https://wzm206.github.io/latent-world-model-nav. --- ### Invocation-Level Reliability of Tool-Using Agents Authors: Afiya Noorain, Subhranshu Mohanty, Amritesh Banerjee, Abhijit Dasgupta Published: 2026-08-23 Categories: cs.AI, cs.CL, cs.LG arXiv: https://arxiv.org/abs/2608.26189v1 Tool-using agents fail two ways: choosing the wrong tool, or forming wrong arguments, and an early failure of either kind can silently corrupt everything downstream. We measure a correct-invocation rate that separates the two, under both a clean teacher-forced context and the model's own free-running context, on five open-weight models over contamination-free multi-step tasks (depths 1-8). By depth 6, roughly 70% of a model's own clean-context capability is lost to its own earlier mistakes (L6 = 0.686, 0.684). Our central finding concerns the measurement itself. Under exact-match scoring against a fixed gold trajectory, a propagation model's severity and recovery parameters are not merely hard to estimate - they are fixed by the scoring rule. Severity is forced to its boundary (0 of 869 poisoned steps correct); recovery is structurally unobservable (0 of 580 poisoned steps returned on-track, against an expected 0.0058 by chance). Both follow from one mechanism: post-divergence, the gold value is generated by tool constants the model never sees, so it is information the model cannot derive. A fit run anyway returns 0.92 and 0.73 for a quantity that is exactly 1.000 - confident numbers for a parameter the scoring rule already determined. We give the mechanism and a remedy, conditional-on-state scoring, applied retrospectively to cached completions at zero additional cost, which un-pins severity to interior estimates excluding zero (+0.149, +0.316). --- ### Is Your Neighborhood Safe? Place-based Stigma in Large Language Models' Urban Safety Judgments Authors: Huy Nguyen, Yue Lin Published: 2026-08-23 Categories: cs.AI arXiv: https://arxiv.org/abs/2608.26188v1 Large language models are increasingly used to inform safety decisions in cities, such as where it is safe to walk, rent, or travel. We ask whether such judgments track measured risk or the patterns attached to an urban neighborhood's name. We probe seven instruct-tuned models under three conditions that dissociate name from geography: coordinates-only, name-only, and name+coordinates, across 186 neighborhoods in Los Angeles and Chicago, joined to violent crime and American Community Survey data. First, ratings are nearly flat under coordinates for six of seven models, while names carry most between neighborhood variation and are moderately calibrated to violent crime; only at frontier scale does the coordinate channel show appreciable variation. Second, names lower safety ratings more for neighborhoods with higher shares of the locally dominant marginalized group (percent Black in Chicago, percent Hispanic in Los Angeles), and this name effect tracks demographic share in all seven models and both cities. In Los Angeles, where demographic share and crime are more separable, the effect survives controls for crime and income and is confirmed by crime-matched pairs. An enforcement-elasticity analysis further shows that over-caution tracks near-fully-reported homicide rather than discretionary, deployment-driven offenses. Third, the effect scales with geographic knowledge: models that better distinguish real neighborhoods apply more demographic stereotype to them. Because neighborhood names carry both genuine crime signal and demographic stereotype, removing names reduces both bias and accuracy. We discuss implications for deploying LLMs in advice and decision-support settings. --- ### GreenLeaf Law Embed Tiny: A Compact Embedding Model for Legal Domain Retrieval Authors: Surya Saka Published: 2026-08-23 Categories: cs.LG, cs.AI, cs.CL arXiv: https://arxiv.org/abs/2608.24936v1 We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal Embedding Benchmark (MLEB) and 64.38% on MTEB(Law, v1),demonstrating competitive performance among models under 1B parameters. Our approach combines a two-stage training pipeline that first distills knowledge from a larger teacher model into a compact student architecture, then applies domain-specific fine-tuning with hard negative mining; a carefully curated dataset of 3.4 million query-passage pairs, including 150,000 human-curated samples across diverse legal jurisdictions; and an efficient inference architecture supporting multiple quantization levels (BF16, INT8, binary) enabling deployment in resource-constrained environments. We provide detailed analysis of our training methodology, architectural choices, and comprehensive evaluation across legal retrieval tasks. Our results demonstrate that domain-specific training with high-quality data can improve performance for specialized domain applications ---