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新论文 · v1arXiv:2611.14263

Chain of Physics: Supporting Fault Diagnosis with Engineering-Inspired Large Language Model Reasoning

Wenqi Zhou, Leonardo Baldo, Kai Goebel, Manuel Arias Chao

原文摘要

Fault diagnosis in complex systems requires linking observed changes in physical quantities to the failure mechanisms that produce them. Experienced engineers perform this task by combining sensor information with knowledge of system behavior and fault propagation. Large Language Models (LLMs) can support this process, but their diagnostic reasoning depends on the availability of asset-specific engineering knowledge and on how this knowledge is represented and used during inference. This work introduces Chain of Physics (CoP), an LLM-based fault-diagnosis framework built around a Constrained Knowledge Graph (CKG) generated automatically from asset-specific reference documentation. CoP addresses failure-sensor association from textual engineering knowledge. The CKG stores diagnostic chains connecting failure modes, changes in physical quantities, and monitoring sensors. During inference, semantically similar graph paths are retrieved for each question and used as both information and in-context examples for the reasoning process. Candidate failure modes are extracted from the question and analysed independently through dedicated LLM calls, which generate plausible failure-to-observation chains. A final decision module compares these chains with the available answer choices. CoP is evaluated on the FailureSensorIQ benchmark across four industrial asset classes and three LLMs. Compared with direct prompting and conventional Retrieval-Augmented Generation (RAG), CoP improves diagnostic accuracy across all evaluated models, with gains ranging from 7.8 to 19.6 percentage points and from 11.7 to 18.2 percentage points, respectively. The results show that structured engineering knowledge can improve LLM-based diagnostic reasoning when graph retrieval is combined with in-context examples of fault propagation.

cs.LG · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14262

Recurrent Modular Structure in Spiking Neural Network Reinforcement Learning

Vikram Ramavarapu, Himanshu Udupi, Ashish Gautam

原文摘要

Biological neural circuits are both modular and recurrent, with dense communities sparsely linked to one another. Spiking neural networks (SNNs) for reinforcement learning are usually feed-forward, and it is unclear how to obtain functional modules in a recurrent SNN or what they buy. In this work, we introduce a class of spiking networks, called Community Recurrent SNNs (CoRe SNNs): recurrent networks partitioned into communities with fixed roles. A controller community receives the stimulus, each action has its own community, and latent communities carry internal state. Spiking activity is then a walk between communities, and the action is the community the walk reaches. All learning is local and three-factor, and the recurrent communities are closer to cortical microcircuits than a feed-forward actor. We compare three regimes for obtaining the communities: dynamic modularity, in which a new learning rule we introduce, Map-STDP, encourages modular structure during learning; partial modularity, in which a stochastic block model (SBM) initialization enforces it partly; and full modularity, in which sealing enforces it fully. On CartPole, across three spiking neuron models, Map-STDP improves learning over the same network without it matches full modularity (sealed communities) on two of the three models, and makes the observed spike walk more compressible. Replacing the feed-forward actor of a TD-STDP actor-critic with recurrent action communities improves on the feed-forward network on CartPole and LunarLander, and matches it on Acrobot. How strongly to enforce modularity depends on the task: full modularity helps most on Acrobot, while on LunarLander the dynamic, partial and full regimes perform alike. Overall, these recurrent modular networks can match or even improve upon feed-forward approaches, with improvements in bio-plausibility and energy efficiency.

cs.AI · cs.LG · cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14257

Elastic-WAM: Adaptive Spatiotemporal Imagination for Efficient Robot Control

Haitian Li, Tianfu Li, Xuxi Zhao, Zizheng Liu, Yangcheng Yu, Yiwen Hou, Zeyu Xiao, Mutian Xu 等 11 位作者

原文摘要

World-Action Models improve robot control by predicting future visual dynamics, but iterative video denoising incurs substantial inference overhead. Recent approaches reduce this cost through partial future denoising, yet typically rely on fixed refinement budgets. Our empirical observations reveal that preferred refinement depths vary across tasks and states, while different future regions exhibit varying relevance to action generation. Motivated by these findings, we propose Elastic-WAM, which adaptively allocates world refinement computation across temporal denoising time and spatial future space. A unified spatiotemporal refinement demand field governs when to stop world refinement, how far to advance each denoising step, and which regions to refresh. We parameterize this field with a shared temporal basis and spatial amplitudes, and learn it through a three-stage action-grounded supervised procedure. At inference, the learned field guides recurrent sparse world refinement to improve task success while reducing inference overhead. Evaluations across diverse simulation benchmarks and real-world long-horizon manipulation demonstrate state-of-the-art control performance with reduced inference latency.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14256

A Cross-Scenario Survey and Analysis of Self-Evolving Multimodal Agents

Jingjing Jiang

原文摘要

Recent work shows that multimodal agents increasingly self-improve without recurring human annotation. However, each self-improvement mechanism is designed and evaluated within a single scenario, leaving its preconditions unverified under cross-scenario transfer. This survey investigates the structural source of asymmetric mechanism transfer across four scenarios: multimodal reasoning, multimodal generation, GUI and Web navigation, and embodied manipulation. We first formalize the self-evolving multimodal agent by four criteria and a differentia of self-reference, and adopt the three-stage self-evolving loop of experience generation, feedback acquisition, and update consolidation as our analytical framework to position the scenarios along ceilings on rollout count and feedback fidelity. Comparing self-evolving with fixed loops within each scenario separates the limitations that the scenario imposes from those inherent in the self-evolving design. We then recast each mechanism as a trade-off that raises a scarce factor by presupposing a task-structural resource, such as output equivalence or checkable outcomes, and judge it portable, conditional, blocked, or redundant across scenarios. We attribute the asymmetry to divergent combinations of scarce factors and resources across opposite transfer directions, and identify the matched-budget measurements that could overturn these still untested verdicts.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14254

Location Matters: Sparse Latent-Variance Attacks for Face Privacy against Diffusion Personalization

Shuting Yang, Shuxin Li, Linyan Dai, Xinwei Zhang, Dong Huang, Renyang Liu, See-Kiong Ng

原文摘要

Text-to-image diffusion models can learn and reproduce an individual's identity from only a few reference photos, creating risks of unauthorized and misleading content generation. Proactive defenses address this risk by adding adversarial perturbations to facial images before release, thereby disrupting identity acquisition during downstream personalization. However, existing methods typically rely on dense $L_\infty$-bounded perturbations, whose accumulated distortions can become visible around facial structures. Recent methods improve protected-image quality, but often at the cost of reduced protection effectiveness. In this paper, we propose Sparse Encoder Attack on Latent Variance (SEAL), a sparse adversarial protection framework that modifies only 5% of the spatial locations in a facial image. SEAL combines a differentiable top-k masking scheme that jointly optimizes perturbation locations and values with a variance-oriented objective over the frozen VAE encoder's latent distribution to disrupt identity-consistent representations. A saliency-guided dispersion penalty further discourages perturbation concentration in perceptually salient facial regions. Experiments on CelebA-HQ and VGGFace2 show that SEAL achieves the best protected-image quality among the compared methods, exceeding 41 dB PSNR with 0.97 SSIM and at most 0.07 LPIPS, while reducing identity similarity to at most 0.15 after diffusion personalization. These results demonstrate that sparse location optimization enables strong identity protection while preserving high visual quality. Code and data are available at https://github.com/Lvxan/SEAL

cs.CV · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14252

Semantic Chains: From Reachable Facts to Justified Answers

Misha Andreev

原文摘要

How can the facts that justify an answer be found from a question and presented as a sequence of connected statements? This paper models a context as a graph of logical facts and takes as given which expressions represent each fact. A parent fact can express a relation among its subfacts, and a preserving edge allows the content of a subfact to be recovered within a full representation of its parent. Suppose a root fact justifies the full answer and a path of preserving edges leads from that root to a fact that designates an object shared by the question and the answer. Under the conditions of the model, a search starting from the objects in the question reaches the root and covers every fact in the selected expansion. The root then admits a text that forms a single link connecting the question to the full answer and justifying it. For chains with several links, the paper gives a complete enumeration of candidate orders within the stated class and specifies when the link texts jointly preserve the justification and support every transition.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.12247

Beyond Third-Person Audits: Situated Interaction Auditing for User-Centered LLM Bias Research

Andrés Abeliuk, Cinthia Sanchez Macias, Valentina Alarcón, Álvaro Madariaga, Claudia Lopez

原文摘要

Research on bias in large language models (LLMs) has predominantly focused on third-person audits, which study how models represent or evaluate demographic groups as external subjects. However, this paradigm overlooks a structural blind spot because the user is absent from the audit. In practice, LLMs are used in open-ended, personal interactions, during which the model implicitly represents the user and adjusts its responses accordingly. When identical requests yield different responses depending on who is asking, bias manifests not in how the model describes others but in how it treats its interlocutor. Empirical first-person bias studies are multiplying, but they use different cues, outcomes, and validation choices, which makes their results hard to compare. We propose Situated Interaction Auditing (SIA), a shared diagnostic framework for this literature. It introduces a typology of user profile signals---names and surface markers, writing style and dialect, and stated identity---together with design principles and outcome measures that let studies be compared and accumulated. We instantiate the framework in a case study of Chilean first and last names across nine task domains and three models, and we close with guidance for practitioners and open questions for researchers.

cs.CY · cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.11911

Understanding Sample Efficiency in Predictive Coding

Gaspard Oliviers, Elene Lominadze, Rafal Bogacz

原文摘要

Predictive Coding (PC) is an influential account of cortical learning. Much of recent work has focused on comparing PC to Backpropagation (BP) to find whether PC offers any advantages. Small scale experiments show that PC enables learning that is more sample efficient and effective in many contexts, though a thorough theoretical understanding of the phenomena remains elusive. To address this, we quantify the efficiency of learning in BP and PC through a metric called ``target alignment'', which measures how closely the change in the output of the network is aligned to the output prediction error. We then derive and empirically validate analytical expressions for target alignment in Deep Linear Networks. We show that learning in PC is more efficient than BP, which is especially pronounced in deep, narrow and pre-trained networks. We also derive exact conditions for guaranteed optimal target alignment in PC and validate our findings through experiments. We study full training trajectories of linear and non-linear models, and find the predicted benefits of PC persist in practice even when some assumptions are violated. Overall, this work provides a mechanistic understanding of the higher learning efficiency observed for PC over BP in previous works, and can guide how PC should be parametrised to learn most effectively.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14247

Personalized Affective Perception via Continual Learning in Human-Agent Negotiation

Mehmet Onur Keskin, Berk Buzcu, Nikhil Churamani, Reyhan Aydogan, Hatice Gunes, Catholijn Jonker

原文摘要

Self-interested intelligent agents that negotiate with humans should interpret nonverbal expressions and adapt their behavior accordingly. However, generic facial-affect models degrade across individuals and contexts, creating a generalization gap. We introduce an affect-adaptive negotiation strategy that incorporates Continual Learning with Imagination for Facial Expression Recognition (CLIFER) to personalize valence-arousal perception in real time. Differential feedback converts changes in valence-arousal direction and intensity into adjustments of the agent's target utility. An asynchronous architecture updates CLIFER in the background during live interaction. In a counterbalanced within-subject study in which 66 participants negotiated with a humanoid robot, the CL-enabled agent achieved significantly higher individual utility and elicited higher participant concession rates than the FaceChannel baseline. The CL condition yielded significantly higher mean arousal estimates, alongside a broader output range. Participant utility and Nash distance did not change significantly between conditions. These outcomes support the practical value of coupling continual affective personalization with differential feedback for real-time negotiation.

cs.AI · cs.HC · cs.LG · cs.MA · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14246

SinkState: On-the-Fly SVD of Recurrent States for Efficient Hybrid LLM Serving

Haolin Jiang, Weihang Li, Zhongzhu Zhou, Jinwen Pan, Santosh Pandey, Hang Liu

原文摘要

Hybrid models combine softmax attention with linear attention, which compresses history into fixed-size recurrent states to balance model quality and inference efficiency. However, linear attention has not pushed this compression to its limit: we find that the queries, keys, and values of linear layers concentrate in a stable, low-dimensional subspace, a phenomenon we call the linear attention sink, so each state is effectively low-rank, with a rank that varies across states. Yet existing serving systems keep these redundant states dense, reading and rewriting them in full at every decoding step, so their memory traffic grows with the batch size and dominates the decoding cost. Thus SinkState stores each recurrent state in an adaptive low-rank form throughout inference with buffered updates and a local SVD restricted to the top subspace. SinkState further consolidates the remaining components asynchronously on the CPU. On four hybrid models with Mamba-2 and Gated DeltaNet (GDN) layers, from 4B to 120B parameters, SinkState reduces the peak memory of recurrent states by up to 2.6x (1.9x on average) and speeds up decoding by up to 1.58x (1.25x on average) over Mamba-SSM and FLA, while maintaining accuracy.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14244

An AI Assistant for Automating Anti-Corruption Expertise of Draft Legislation Based on the TRACE Framework

Serhii Zabolotnii, Alina Tarasova

原文摘要

We present an AI assistant for automating anti-corruption expertise of draft legislation based on the TRACE architecture, designed to screen bills for corruption risks: pinpoint provisions an expert would criticise and name the regulatory defect in each. The pipeline combines a deterministic parsing core, model-guided single-provision qualification, rule-based factor assignment, and external human oversight. To evaluate the assistant, we constructed an open benchmark from the published conclusions of Ukraine's National Agency on Corruption Prevention (NAZK), comprising 132 draft laws with 474 model-extracted findings. A system flag agrees with a finding when it targets a compatible provision and an LLM judge rates the risk as the same (strict) or partly the same (lenient). On 100 draft laws, the pipeline matches 0.28 of the findings within its top ten flags using the local Gemma 4 31B model, 0.30 with Sonnet 5.5, and 0.43-0.46 across all flags (non-blind; the frozen blind baseline achieves 0.21, 95% CI 0.16-0.26). Across all runs, most misses occur on provisions that the system located but qualified as a different risk, and none of the 32 legal-collision findings was matched. On 21 acts held out from tuning, the pipeline outperforms a monolithic prompt containing the full methodology under the strict measure, though not under the lenient one, and Opus 5.5 adds nothing over Sonnet. The reported scores reflect agreement with model-extracted findings rated by an LLM judge whose two access routes agree on 73 of 74 labels, without human expert validation.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14239

On Federated Averaging with Partial Participation under a General Variance Condition

Ziming Wang, Kun Huang, Karl Henrik Johansson, Shi Pu

原文摘要

In this paper, we study the convergence of Federated Averaging (FedAvg) with partial participation under a general variance condition. In each communication round, the server samples $S$ of the $n$ clients uniformly at random without replacement. The stochastic gradients are assumed to satisfy the so-called ABC condition, which allows their variance to grow with the local objective gaps. For smooth nonconvex objectives, we establish a finite-time stationarity bound in terms of the heterogeneity measure $σ_f^\star:=f^\star-\frac{1}{n}\sum_{i=1}^n f_i^\star$ and the client sampling factor $I_S:=(n-S)/(n-1)$. Here, $f^\star$ and $f_i^\star$ denote the infima of the global average objective and client $i$'s local objective, respectively. The bound exhibits a linear speedup in the number of sampled clients $S$ and recovers the full-participation result when $S=n$. Under diminishing stepsizes, we further prove that the gradient norm converges to zero both in expectation and almost surely. Notably, compared with existing analyses of FedAvg with partial participation, our results require no additional data heterogeneity assumption such as bounded gradient dissimilarity (BGD), and the sampling-induced term in our bound is not amplified by the number of local updates at the leading order. The importance of removing the BGD assumption is illustrated by an example that is smooth and lower bounded but violates BGD, yet is covered by our analysis.

math.OC · cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.13414

FUTON: Fourier Tensor Networks for Implicit Neural Representations

Pooya Ashtari, Pourya Behmandpoor, Nikos Deligiannis, Aleksandra Pizurica

原文摘要

Implicit neural representations (INRs) have emerged as powerful tools for encoding signals, yet dominant MLP-based designs often suffer from slow convergence, and how their accuracy depends on their size is known only empirically. We introduce FUTON (Fourier Tensor Network), which models signals as generalized Fourier series whose coefficients are parameterized by a low-rank tensor decomposition, read out by a linear or MLP head. FUTON combines complementary inductive biases: Fourier bases capture smoothness and periodicity, while the low-rank parameterization enforces low-dimensional spectral structure. Beyond universal approximation, we show that with a linear head, the best FUTON's error splits exactly into the truncation of the signal's series and the best low-rank error of its coefficients, which we bracket from the samples before training, with a FUTON that attains the upper bound. On image fitting, occupancy volumes, and novel view synthesis, against twelve INRs of matched size, FUTON achieves the best average quality, reaches the final quality of each task's strongest baseline 4-34x sooner than that baseline, infers fastest, and trains in the least memory.

eess.IV · cs.CV · cs.LG · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14236

StaRSE: Compact Russian Sentence Embeddings with a Sign-Coded Static Encoder

Boris Malashenko, Ivan Jarsky, Egor Soldatov, Vladislav Luzhnov, Sviatoslav Stumpf, Vladislav Shmatkov, Viacheslav Shalamov, Andrey Stankevich 等 9 位作者

原文摘要

Transformer sentence encoders provide strong retrieval quality, but their inference cost becomes a bottleneck when embeddings must be produced on CPUs or refreshed frequently. We introduce StaRSE, a compact Russian static sentence encoder that removes Transformer inference at deployment time: token directions are stored as sign bits, token norms are stored separately, and sentences are encoded by mean pooling over norm-scaled sign directions. StaRSE is initialized from ruBERT-base, fine-tuned on Russian adjacent-sentence and task-shaped pairs, and post-trained with a contrastive stage that directly optimizes the sign-projected token table. On RuMTEB, StaRSE reaches a task mean of $51.16$, $7.98$ points above the strongest included static baseline. The packaged model occupies $11.3$ MiB and has a mean CPU latency of $0.08$ ms per sentence, compared with an estimated $55.7$ MiB 16-bit parameter footprint and $1.43$ ms mean CPU latency for the closest-quality contextual encoder. Ablations examine adjacent-sentence supervision, token norms, and direct sign-projected post-training.

cs.CL · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.22639

TRACE: Business Rule-Grounded Reasoning Curriculum for Knowledge-Preserving Parametric Tool Retrieval in Enterprise LLMs

Sai Shruthi Sistla, Ashutosh Hathidara, Christopher Toukmaji, Mayank Shrivastava, Karthikeyan Asokkumar

原文摘要

Parametric retrieval enables LLMs to retrieve tools implicitly by assigning each API a unique virtual token and training the model to generate it via constrained beam search. Toolsense shows that this regime has two critical drawbacks: it destroys parametric tool knowledge during training, and its beam-search decoding is too slow for real-time deployment. We introduce TRACE (Tool Retrieval via Augmented Chain-of-thought and Enterprise rules), a two-stage curriculum that resolves this dissociation. Stage 1 reuses the multi-format memorization SFT from ToolSense to seed tool knowledge with LoRA. Stage 2 is our core contribution: the model is trained to emit a thinking trace before producing a JSON list of tool tokens, using two data sources -- RRB pairs from ToolSense and queries synthesized to target business rules curated by domain experts -- both augmented with reasoning traces. This training objective preserves Stage 1 MCQ and QA probing accuracy while enabling single-beam greedy decoding at production latency. Evaluated on a combined enterprise catalog of 8,300+ tools across two enterprise product lines, TRACE training for Stage 2 not only preserves but improves tool understanding: MCQ accuracy gains +3.2 pp and QA probing gains +9 pp over Stage 1. On retrieval, TRACE achieves ~86% recall on Domain A and ~60% on Domain B -- compared to embedding baseline performance of ~27% & ~52% -- both with single-beam greedy decoding, making it directly deployable at production latency.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14220

SHARP: Semantic Hierarchical Agentic Reasoning for Next POI Prediction with Dynamic Memory

Yuhang Zhan, Silin Zhou, Zhuo Chen, Haoxiang Lei, Yunren Feng, Lisi Chen, Shuo Shang

原文摘要

Next point-of-interest (POI) prediction supports personalized location services by anticipating users' next destinations. Recent methods leverage large language models (LLMs) for semantic reasoning and graphs for collective mobility modeling, improving POI prediction. However, three challenges still remain. Bridging LLMs' semantic reasoning and specific POI prediction remains challenging. User mobility also evolves over time, making it necessary to retain and update historical knowledge within limited LLM contexts. Meanwhile, global transition graphs smooth out time-specific category transition patterns. To address these challenges, we propose Semantic Hierarchical Agentic Reasoning for Next POI Prediction with Dynamic Memory (SHARP), an agentic hierarchical framework in which category-level reasoning guides specific POI prediction. A trajectory memory agent maintains a dynamic trajectory memory bank that preserves earlier mobility patterns and tracks behavioral changes with limited context growth. We construct multi-granularity time-aware category transition graphs with global and time-conditioned views to preserve temporal regularities obscured by global aggregation. A category predict agent combines these graphs with trajectory memory to predict likely next categories. SHARP learns semantic IDs (SIDs) through a residual-quantized variational autoencoder (RQ-VAE) to construct semantic POI representations, which are jointly optimized with a category-guided POI predictor end-to-end. On three real-world datasets, SHARP improves Acc@5 and Acc@10 by an average of 5.29 and 4.52 percentage points, respectively, over the strongest baseline.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.27947

Edit in Sync: Unified End-to-End Audio-Visual Editing for Human-Centric Videos

Ziye Yuan, Minghong Cai, Xu He, Ruchang Yao, Xiao Fu, Kaixin Ding, Ming Zhang, Xiangyu Du 等 11 位作者

原文摘要

Human-centric audio-visual editing requires precise alignment of lip movements, gestures, and expressions with speech. Existing methods process inputs through independent video and audio pipelines, preventing cross-modal interaction between visual editing and audio dubbing. Many of these pipelines further rely on mask-based inpainting. As a result, they suffer from poor lip-audio synchronization, cumulative pipeline errors, and an inherent mask-size dilemma. We present AVE, the first unified end-to-end framework that jointly performs reference-guided person swapping, insertion, and deletion across both audio and video modalities without requiring spatial masks. To overcome data scarcity, we design a synthesis pipeline and introduce AVE-Dataset, the first audio-visual editing dataset to provide 100K pairs with appearance and voice references. By combining Synthetic-to-Real reconstruction with preliminary audio warm-up, our framework transcends synthetic pipeline limits and mitigates audio-video token imbalance. AVE outperforms modality-separate audio-visual editing pipelines in editing realism, temporal consistency, and audio-visual alignment.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14211

Verifiable Agentic Traffic Class: A Policed Quality-of-Service Mechanism for Agentic AI Traffic, Evaluated from Simulation to Real-Kernel Adversarial Testing

Naveen Babu Narayanan Nair, Sajay Kurup

原文摘要

Software agents built on large language models generate traffic unlike human web traffic, yet routers have no class for it and cannot verify a sender's claim to be an agent. This paper proposes the Verifiable Agentic Traffic Class (VATC), which combines a reserved Differentiated Services Code Point (DSCP) value declaring agent traffic, a router-side classifier that demotes flows contradicting the declaration to a class served after best-effort traffic, and a rate-limited priority queue. VATC was compared with static Differentiated Services (DiffServ), a dedicated class that trusts the marking, and fq_codel, a per-flow fair-queuing scheduler, in two simulators and on a real Linux kernel. VATC matched DiffServ under steady load. In a constructed burst it cut mean agent queuing delay by 98% relative to DiffServ in simulation (assuming an immediate verdict), at a 12% increase in human delay; most of the gain came from the separate class. Under a spoofed flood in the kernel, the rate limit held human latency 11 times below DiffServ's. Without verification, DiffServ and a tightly configured fq_codel gave legitimate agents lower latency than VATC; a live four-feature classifier then demoted all 20 bulk-shaped spoofers and gave agents 0.30 ms, but missed six spoofers that copied the agent pattern. With emulated network delay, a timing feature of the classifier stopped separating agent from human flows. In real agent harnesses on a congested link, router queueing took 13% to 63% of task time, depending on harness and assumed model latency. The traffic was synthetic or sandbox-scale.

cs.LG · cs.AI · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14210

Tree-Ising: Repairing Language Models with Bit-Level Ising Edits

Hadi Salloum, Ammar Ali

原文摘要

Weight-space programming, introduced in recent MIT work by Takehi et al., enables a frontier LLM agent to read a network's weights and write a repair. Their GPT-6 Astra agent corrected the reasoning loops of Qwen3.5-0.8B without gradients, using 158 agent turns, 89.7 million tokens, and 50 forward-pass requests. Our method, Tree-Ising, casts this repair as a sparse discrete optimization problem over the model's own BF16 weights. Forward statistics define an objective that weakens a loop direction, strengthens a concluding direction, and preserves the layer's output on productive tokens. Within a state-tree representation, where lossless BF16 storage acts as a special case, the admissible edits are sign flips and exponent shifts that preserve all shared group exponents and mantissas. We decompose this objective exactly into independent QUBO (Ising) problems, one per output row, which we solve on a GPU using simulated annealing or quantum-inspired simulated bifurcation. On the agent's two layers, Tree-Ising achieves 54.6% on MATH-500, outperforming the agent's edit (51.7%) under the same evaluation protocol, while the base model scores 7.9% as reported. The simulated bifurcation solver yields a statistically indistinguishable repair at 55.1%. Both solvers outperform the agent edit across all five math benchmarks, with statistically significant improvements on MATH-500 and GSM8K after correcting for multiple comparisons. Our approach modifies 13% to 17% fewer weights and reduces BF16 bit flips by a factor of 2.2 to 2.3. It requires only four forward-pass requests and zero LLM tokens, with the annealing recipe costing about $0.90 in GPU time. On Qwen3.5-2B, with layers and dose selected using only the base model, the same recipe increases greedy GSM8K accuracy from 21.6% to 69.4%. As a proof of concept, we execute an eight-variable repair subproblem using QAOA on an IBM Heron processor.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.16245

AI-Mediated Communication Can Steer Collective Opinion

Stratis Tsirtsis, Kaivalya Rawal, Chris Russell, Brent Mittelstadt, Sandra Wachter

原文摘要

Generative artificial intelligence (AI) is increasingly integrated into the online platforms where humans exchange opinions; large language models (LLMs) have been polishing users' posts on LinkedIn and providing context for content shared on X. While prior work has shown that AI can express biased opinions and shape individuals' opinions during human-AI interactions, less attention has been paid to its influence on collective opinion formation when mediating human-to-human communication. We address this gap via a combination of empirical and theoretical analyses. We show empirically that LLMs from multiple popular families introduce directional biases when instructed to edit human-written texts on contested topics, for example, nudging texts in favor of gun control and against atheism. Building on this observation, we introduce a mathematical model of opinion dynamics in which an AI system sits between users on a social network, transforming the opinions they express and perceive. By analytically characterizing the equilibrium of this model and performing simulations on real social network data, we show that biases introduced by AI in human-to-human communication can be amplified through the network and shift collective opinion in their direction. In light of these findings, we investigate whether such biases are controllable by online platforms. We audit the "Explain this post" feature on X and find evidence of pro-life bias in Grok's outputs on abortion-related content, which we trace back to specific design choices. We conclude with a discussion of the broader implications of our findings in relation to ongoing legislative efforts in the European Union.

cs.CY · cs.AI · cs.CL · cs.LG · cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14209

Breakdown of Autoregressive Multi-Step Reasoning Under Concentrated vs. Diffuse Attention Head Dropout

Karol Struniawski

原文摘要

During extended wakefulness, mammalian neocortical networks experience localized periods of transient neuronal silence termed off-states. Here, we examine how analogous spatiotemporally concentrated disruptions affect sequential reasoning in autoregressive Large Language Models (LLMs). Evaluating 250 paired derivations on the GSM8K benchmark across two 8B models (Qwen3-8B and LLaMA-3.1-8B-Instruct), we contrast clustered bursts of attention head masking against budget-matched diffuse i.i.d. dropout. Concentrated bursts induce substantial reasoning degradation: at onset parameter $\mathcal{B}=0.10$ (scheduling $\sim 5.7\%$ of vulnerable head-token volume), Qwen3-8B accuracy drops from $91.2\%$ under diffuse noise to $78.8\%$ under burst dropout ($Δ= -12.4$ percentage points [pp], exact McNemar $p = 5.5 \times 10^{-6}$), while LLaMA-3.1-8B-Instruct drops from $84.0\%$ to $76.8\%$ ($Δ= -7.2$ pp, $p = 0.0198$). Conversely, dispersing the identical cumulative budget uniformly across generation steps via stratified quintile matching yields substantially milder degradation (retaining $91.2\%$ in Qwen and $84.0\%$ in LLaMA). In an exploratory factorial dissociation ($N=100$), single-step dropout density is associated with an acute drop in a single schedule draw ($-10.0$ pp on Qwen, $p_{\text{holm}} = 0.0425$), while multi-token continuity exhibits an inconclusive compounding trend ($-7.0$ pp, $p_{\text{holm}} = 0.1892$). Error analysis reveals that Qwen failures reflect derivation errors within formatted responses (conditional accuracy drop of $-11.4$ pp), whereas in LLaMA $22.2\%$ of discordant losses lack a final answer format line (conditional gap narrows to $-5.0$ pp). These findings demonstrate that autoregressive reasoning chains are particularly vulnerable to concentrated structural disruptions, providing empirical benchmarks for fault tolerance in transformer architectures.

cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.24969

Frequency Domain Reservoir Computing

Klaus Schertler, Xiomara Runge, Andrea Ceni, David Kappel, Claudio Gallicchio

原文摘要

While the quadratic sequence-length bottleneck of transformers has fueled a resurgence in recurrent models, effectively capturing complex dynamics requires architectures that balance efficient training with highly expressive latent states. Echo State Networks (ESNs) offer a compelling approach by utilizing fixed recurrent weights to circumvent backpropagation through time, enabling a closed-form training solution. However, achieving the expressivity needed for complex tasks demands large reservoirs, exposing an $\mathcal{O}(N^2)$ state-update bottleneck that prevents ESNs from matching the scale of contemporary recurrent models. To address this limitation, we introduce Frequency Domain Reservoir Computing (FRESCO), an ESN architecture operating entirely in the frequency domain while avoiding domain-shift overheads to achieve $\mathcal{O}(N)$ complexity for dense, non-linear updates via structured circulant recurrence. By employing a novel dimensional zero-padding input embedding, a packed frequency-domain readout, and a natively applied frequency-domain non-linearity, FRESCO drastically reduces computational costs and energy consumption of training and inference. Furthermore, FRESCO achieves competitive predictive performance on memory benchmarks, sequential classification, and multivariate long-horizon forecasting, offering a scalable and energy-efficient path forward for dense recurrent architectures.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.27932

Binary Quantized Neural Network Training Is W[1]-Hard Parameterized by Input and Output Dimensions

Tao Jiang, Minbo Gao, Shaowei Cai

原文摘要

When the weights and biases of a neural network range over a fixed finite alphabet, training the network to fit a data set exactly becomes a finite combinatorial problem, and parameterized complexity asks which structural quantities of an instance make this problem tractable. Ganian, Sommer, and Sorge (ICLR 2026) showed that training quantized networks is fixed-parameter tractable when parameterized jointly by the input dimension $α$, the output dimension $ω$, and the treewidth of the architecture, and they asked whether $α+ω$ alone suffices. We show that it does not: training binary quantized networks is W[1]-hard parameterized by $α+ω$, so it admits no algorithm with running time $f(α+ω)\cdot|I|^{O(1)}$ for any computable $f$ unless FPT = W[1]. Hardness holds already for exact fitting of $k+1$ examples whose inputs equal their targets and form a coordinatewise prefix chain, and also when all biases are fixed to zero. The proof rests on a one-flip routing equivalence. On such data, nonnegative binary weights force every neuron to switch on at most once, and a network fits the data exactly if and only if the architecture contains vertex-disjoint paths that link each changing input to the corresponding output. Every neuron therefore has one of only $k+1$ activation profiles, yet deciding whether these profiles can be realized simultaneously is as hard as disjoint-path routing. Small input and output dimensions thus limit what each neuron can do locally, but not how the architecture forces neurons to share resources globally.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.06466

Diversity Curves for Graph Representation Learning

Katharina Limbeck, Nadja Häusermann, Martin Carrasco, Guy Wolf, Bastian Rieck

原文摘要

Graph-level representations are crucial tools for characterising structural differences between graphs. However, comparing graphs with different cardinalities, even when sampled from the same underlying distribution, remains challenging. Unsupervised tasks in particular require interpretable, scalable, and reliable size-aware graph representations. Our work addresses these issues by tracking the structural diversity of a graph across coarsening levels. The resulting graph embeddings, which we denote diversity curves, are interpretable by construction, efficient, and directly comparable across coarsening hierarchies. Specifically, we track the spread of graphs, a novel isometry invariant that is inherently well-suited for encoding the metric diversity and geometry of graphs. We utilise edge contraction coarsening and prove that this improves expressivity, thus leading to more powerful graph-level representations than structural descriptors alone. Demonstrating their utility over a range of baseline methods in practice, we use diversity curves to (i) cluster and visualise simulated graphs across varying sizes, (ii) distinguish the geometry of single-cell graphs, (iii) compare the structure of molecular graph datasets, and (iv) characterise geometric shapes.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07342

Selective Retrieval Routing for Multi-Violation Knowledge Graph Repair

Tung-Wei Lin, Gabe Fierro, Han Li, Tianzhen Hong, Pierluigi Nuzzo, Alberto Sangiovanni-Vincentelli

原文摘要

Knowledge graphs often contain missing or inconsistent facts that undermine the applications built on them. Schema validation detects such violations but does not identify which of several valid repairs recovers the intended graph. External corpora can supply the evidence needed to identify the intended repair, but retrieving from them is costly and not always successful. We propose a framework for selective retrieval in iterative graph repair. An LLM repair agent explores and edits the graph, while a router controls its access to a researcher agent that retrieves external evidence. We extend a violation-injection framework to generate cases with multiple compatible violations and known reference repairs. The agent's repair outcomes on these cases provide supervision for training the deployment-specific router. On two building-automation datasets, researcher access improves repairs for some violations and degrades them for others, with effects differing across deployments. The router uses fewer repair-time tokens than always enabling access in all six evaluation suites. It achieves higher repair $F_1$ than always disabling access in five of six suites.

cs.DB · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2504.01551

Identifying Macro Causal Effects in a C-DMG over ADMGs

Simon Ferreira, Charles K. Assaad

原文摘要

Causal effect identification using causal graphs is a fundamental challenge in causal inference. While extensive research has been conducted in this area, most existing methods assume the availability of fully specified directed acyclic graphs or acyclic directed mixed graphs. However, in complex domains such as medicine and epidemiology, complete causal knowledge is often unavailable, and only partial information about the system is accessible. This paper focuses on causal effect identification within partially specified causal graphs, with particular emphasis on cluster-directed mixed graphs (C-DMGs) which can represent many different acyclic directed mixed graphs (ADMGs). These graphs provide a higher-level representation of causal relationships by grouping variables into clusters, offering a more practical approach for handling complex systems. Unlike fully specified ADMGs, C-DMGs can contain cycles, which complicate their analysis and interpretation. Furthermore, their cluster-based nature introduces new challenges, as it gives rise to two distinct types of causal effects: macro causal effects and micro causal effects, each with different properties. In this work, we focus on macro causal effects, which describe the effects of entire clusters on other clusters. We establish that the do-calculus is both sound and complete for identifying these effects in C-DMGs over ADMGs when the cluster sizes are either unknown or of size greater than one. Additionally, we provide a graphical characterization of non-identifiability for macro causal effects in these graphs.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.14786

Known By Their Actions: Fingerprinting LLM Browser Agents via UI Traces

William Lugoloobi, Samuele Marro, Jabez Magomere, Joss Wright, Chris Russell

原文摘要

As LLM-based agents increasingly browse the web on users' behalf, a natural question arises: can websites passively identify which underlying model powers an agent? Doing so would represent a significant security risk, enabling targeted attacks tailored to known model vulnerabilities. Across 14 frontier LLMs and four web environments spanning information retrieval and shopping tasks, we show that an agent's actions and interaction timings, captured via a passive JavaScript tracker, are sufficient to identify the underlying model with up to 96\% F1 within a fixed harness. Although fingerprints are harness-specific, with cross-harness transfer falling to near chance, pooling traces across harnesses recovers model identification at 0.74 to 0.81 Macro-F1. We further show that strong classifiers can be trained from roughly one third of the available traces, and that identification improves steadily as more events are observed. Injecting randomised timing delays between actions substantially degrades classifier performance, but does not provide robust protection: a classifier retrained on delayed traces largely recovers performance. We release our code and a labelled corpus of agent traces at https://github.com/KabakaWilliam/known_actions.

cs.CR · cs.AI · cs.HC · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14196

Joint Quantization and Dynamic Partitioning for Context-Aware Edge Vision Transformers

Zhu Jiaqi, Jain Prakriti, Katare Dewant, Kumar Saurabh

原文摘要

Deploying vision transformer models across connected and automated vehicles (CAVs) requires joint management of computation and Vehicle-to-Vehicle (V2V) communication. Static split-inference methods use fixed partition points and cannot adapt to time-varying link bandwidth, processing load, and concurrent multi-stream traffic. This paper proposes an edge orchestration framework that jointly selects model partition points, per-layer quantization bitwidths, and execution placement across heterogeneous vehicular compute nodes. The joint partitioning and mixed-precision allocation problem is formulated as a Mixed-Integer Linear Program (MILP) that minimizes compute and communication energy subject to end-to-end latency and task-quality constraints. The framework is evaluated on Jetson Orin Nano (8~GB), Jetson Orin NX (16~GB), and Jetson AGX Orin (32~GB) nodes using OPV2V-driven multi-stream workloads with $K \in \{2,4,6,8\}$. Across the evaluated configurations, the proposed formulation reduces V2V communication energy by $33.3$--$36.0\%$ relative to a Uniform INT8 split. At $K=8$, end-to-end task completion latency is reduced by $34.0\%$ on the Orin Nano and $33.1\%$ on the Orin NX relative to the same baseline, while limiting the reduction in 3D detection accuracy to $3.1$--$4.1$ percentage points in $\mathrm{mAP}_{50}$ relative to FP16 execution.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.03623

Self-supervised Physics-Informed Manipulation of Deformable Linear Objects with Non-negligible Dynamics

Youyuan Long, Gokhan Solak, Sara Zeynalpour, Heng Zhang, Arash Ajoudani

原文摘要

We address dynamic manipulation of deformable linear objects by presenting SPiD, a physics-informed self-supervised learning framework that couples an accurate deformable object model with an augmented self-supervised training strategy. On the modeling side, we extend a mass-spring model to more accurately capture object dynamics while remaining lightweight enough for high-throughput rollouts during self-supervised learning. On the learning side, we train a neural controller using a task-oriented cost, enabling end-to-end optimization through interaction with the differentiable object model. In addition, we propose a self-supervised DAgger variant that detects distribution shift during deployment and performs offline self-correction to further enhance robustness without expert supervision. We evaluate our method primarily on the rope stabilization task, where a robot must bring a swinging rope to rest as quickly and smoothly as possible. Extensive experiments in both simulation and the real world demonstrate that the proposed controller achieves fast and smooth rope stabilization, generalizing across unseen initial states, rope lengths, masses, non-uniform mass distributions, and external disturbances. Additionally, we develop an affordable markerless rope perception method and demonstrate that our controller maintains performance with noisy and low-frequency state updates. Furthermore, we demonstrate the generality of the framework by extending it to the rope trajectory tracking task. Overall, SPiD offers a data-efficient, robust, and physically grounded framework for dynamic manipulation of deformable linear objects, featuring strong sim-to-real generalization.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19013

Data Density Scaling Laws for Image Self-Distillation

Alvard Barseghyan, Ani Vanyan, Hakob Tamazyan, Hrant Khachatrian

原文摘要

Current scaling law formulations suggest that increasing the number of unique samples in a dataset consistently improves model performance, while repeated exposure to the same samples provides diminishing value. In this work, we challenge this assumption for image self-distillation models. We train iBOT models on random subsets of seven datasets spanning satellite imagery, driving scenes, egocentric video, and natural images, and evaluate their frozen representations on downstream tasks. We propose a density-interaction scaling law that combines unique-image count $U$ and repetition $R$, fitting it on runs with at most 50 million image presentations and testing it at larger budgets $C$. On Maxar dataset, a random subset containing approximately 1.5% of the training pool improves average nearest-neighbor classification accuracy by 3.11 percentage points over training on the full pool at the same budget of 200 million image presentations. Smaller subsets also outperform the full pool on SATLAS and Ego4D, whereas ImageNet favors larger pools at higher budgets. At a fixed subset size, further training can improve performance, lead to a plateau, or reduce performance. Our law captures smaller-subset advantages that the compared baselines cannot represent and improves prediction at higher budgets on Maxar, SATLAS, and Ego4D under nearest-neighbor evaluation. These findings show that both the number of unique images and how often they are repeated matter when allocating a training budget.

cs.LG

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