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新论文 · v1arXiv:2611.16218

A Coupled Control Barrier Function Approach to Deadlock-Free Multi-Robot Conflict Resolution

Gary Rostomyan, Ketan Savla, Petros A. Ioannou

原文摘要

Many robotic applications involve scenarios where multiple robots interact and the decisions of one robot influence the behavior of others. This paper presents a quadratic programming (QP)-based feedback controller for multi-robot conflict resolution with input constraints. Safety is enforced using coupled Control Barrier Function (CBF), while a yielding matrix encodes how robots influence each other. Unlike existing methods with fixed or hard priorities, the proposed framework enables yielding preferences while guaranteeing deadlock-free operation by design. Sufficient conditions are established for the controller to be locally Lipschitz. The optimal CBF sign- indefinite slack is shown to admit a finite uniform bound, which is compensated through an enlarged CBF safety margin to obtain a forward invariant set contained within the desired safe set. The analysis also identifies an irreducible slack component due to finite maximum deceleration that cannot be eliminated by increasing the slack penalty. Finally, sufficient conditions on the yielding matrix and nominal controller are derived to exclude deadlock states. Simulations demonstrate adaptive coordination across yielding preferences and robustness to robot failures.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16217

WorldDraft: If Code Builds the World, Can Video Models Just Render It?

Zhiyu Hou, Wen-Jie Shu, Harold Haodong Chen, Weilin Ruan, Chenfei Liao, Liuzhou Zhang, Disen Lan, Xiaogang Xu 等 10 位作者

原文摘要

Multimodal coding agents decouple 4D world construction from visual generation, repurposing video models from world generators toward generative renderers. This division of labor raises a fundamental question: if code builds the world, can video models just render it? A reliable generative renderer hinges on two complementary criteria: (i) world preservation, retaining prescribed 4D kinematics and camera paths; and (ii) visual realization, persistently binding text-specified target attributes over time. To evaluate this capability, we introduce WorldDraft, a benchmark of $432$ strictly paired test cases across $48$ executable 4D world families spanning six dynamic challenge categories. Under a $3 \times 3$ factorial design, WorldDraft orthogonally crosses draft granularity (abstract proxy to previs) and appearance alignment (neutral to conflicting) under identical dynamics. Extensive evaluations across $12$ representative models reveal that: (I) world preservation and visual realization remain in stark operational tension; (II) richer draft scaffolding anchors structure but paradoxically impairs appearance fidelity; and (III) conflicting source cues trigger severe source leakage (up to $64\%$), exposing fragile cross-modal disentanglement. To mitigate these bottlenecks, we propose WorldDraft-Primer, a plug-and-play visual prompting strategy that consistently improves both objectives across frozen video renderers. Together, WorldDraft and WorldDraft-Primer lay a foundation for bridging programmatic 4D simulation with video generation, advancing physically grounded generative rendering.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v8arXiv:2606.03163

OpenAgenet / OAN Yellow Paper: Technical Architecture for Trust-Governed Resource Identity and Discovery

Jinliang Xu

原文摘要

This yellow paper specifies the technical architecture and verification semantics of OpenAgenet (OAN), a protocol-neutral trust layer for heterogeneous Agent resources. The design combines did:oan decentralized identifiers (DIDs), verifiable credentials (VCs), on-chain infrastructure governance, and Root-verified resource packages. It defines the objects and lifecycle transitions that connect subject-control verification, Registrar admission, Root acceptance, authorized Discovery, and signed invocation. Infrastructure authorization requires both active governance state and a valid Root-issued credential; resource discovery additionally requires package integrity, current-version state, and authorized-domain coverage. Local OAN Identity signing material remains separate from public protocol objects. Integration profiles carry resource identity into the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol through Skill, Runtime, Sidecar, and Gateway interfaces without redefining native task semantics. The paper distinguishes protocol requirements from implementation choices and sets out security properties, persistence and recovery expectations, and conformance criteria. Together, these rules provide a technical basis for independently implemented registration, discovery, and identity-protected interaction within an OAN trust domain.

cs.MA · cs.AI · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16215

Guided Generation of Linux Kernel Semantic Patches with Small Local Language Models

Mohanned Ahmed

原文摘要

When a Linux kernel API changes, hundreds of drivers are converted by hand over months, and the earlier conversions are reviewed examples for the later ones. We ask whether small open language models running on a laptop can turn such examples into Coccinelle semantic patches, the rules kernel maintainers already use for tree-wide changes. We adapt guided code generation to this task: the training examples are decomposed by script into a tree of change variants, the model writes one small rule per variant under feedback from applying it to the examples, deterministic components repair SmPL syntax and generalise driver-specific names, rules are composed greedily, and a compile critic repairs or rejects a rule whose output does not build. We evaluate on a pre-registered test set of 7 migrations whose new APIs appeared after the training cutoffs of all three models (Qwen2.5-Coder 7B, Llama 3.1 8B, Gemma 3 4B), with 122 held-out driver conversions, counting a conversion as solved only if the rule's output matches the human fix and compiles. With the framework, Qwen2.5-Coder solves 35.3 conversions on average over three seeds (range 32-41), against 1.0 when prompted directly; Spinfer solves 41 and a frontier LLM agent 66. The local model solves fewer conversions than Spinfer but its rules are the most precise on the test set (0.74 of the files they change are solved, against 0.55 for Spinfer and 0.70 for the frontier agent), and the compile critic removes every output that matches the human fix but does not build. The two general-purpose models stay weak even with guidance (at most 12 solved). All code, prompts, model responses and results are released.

cs.SE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16213

Visibility-Aware Task and Motion Planning with Task-Centric Perception

Sahit Chintalapudi, Xiaoyan Wang, Aryan Zoroufi, Aditya Agarwal, Nancy Kanwisher, Tomás Lozano-Pérez, Leslie Pack Kaelbling

原文摘要

Long-horizon manipulation under uncertainty remains a challenging problem in robotics. We present \ourmethod{}, an algorithm for Visibility-Aware Task and Motion Planning. \ourmethod{} extends existing deterministic task and motion planning solvers to partially observable object rearrangement tasks, while still being able to take advantage of the efficiency and compositional generalization over long horizons of the base solver. This extension interleaves sensing actions into a plan skeleton that assumed full observability, and uses a novel persistent object-based memory to fuse observations into object mesh hypotheses and an observed occupancy map. We evaluate \ourmethod{} on real-world tabletop rearrangement tasks which feature heavy occlusion and partial visibility and demonstrate that it outperforms baselines that do not perform active perception or have persistent memory.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16212

SPEM: Scan-Prior Entropy Modeling for LiDAR Point Cloud Compression

Bojun Liu, Yangzhi Ma, Jie Li, Dong Liu, Li Li

原文摘要

LiDAR point clouds exhibit scan-induced spatial regularities that are informative for geometry compression. Existing scan-aware approaches often exploit these regularities by transforming coded geometry into spherical or beam-aligned coordinate systems. However, this couples acquisition priors with geometry representation, altering spatial partitioning and quantization, and potentially preventing exact recovery of the original Cartesian geometry. We argue that acquisition priors should guide probability modeling rather than dictate the coding representation. We propose SPEM, a Scan-Prior Entropy Modeling framework that exploits LiDAR scanning regularities within the entropy model without requiring coordinate transformation. We introduce a multi-frequency spherical rotary positional encoding that captures scan-aligned spatial relationships in both backbone self-attention and decoded-context retrieval. Building upon this encoding, we develop a decoded-memory autoregressive predictor that efficiently exploits previously decoded occupancy information through reusable key/value caches. Experiments demonstrate state-of-the-art performance in both lossy and lossless LiDAR geometry compression, highlighting the effectiveness of scan-prior modeling across different coordinate representations.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16211

One Fuzzer, Many Reasoners: Adaptive Neuro-Symbolic Fuzzing with Reliability-Calibrated and Context-Gated Guidance

Jinghao Wen, Xianglong Yu, Shaohuang Wang, Xun Jiao

原文摘要

Neural-symbolic AI (NSAI) combines learned perception with programs, constraints, and compositional reasoning, but remains difficult to test because small semantics-preserving changes can alter grounded symbols and trigger discontinuous reasoning failures. Existing neural fuzzers mainly rely on neural coverage or representation-space feedback and do not provide a common testing interface for heterogeneous NSAI systems. We introduce NSAI-Fuzz, a unified semantics-aware fuzzing framework that separates task-specific valid mutation from architecture-neutral guided search and represents each candidate using five availability-aware, rank-normalized risk signals: boundary pressure, grounding uncertainty, representation drift, constraint violation, and archive novelty. We further propose Reliability-Calibrated Weighting (RCW), which learns dataset-model-level signal allocations from calibrated reliability, and Contextual Signal Gating (CSG), which adapts signal weights to the current candidate pool. We evaluate four NSAI systems across four reasoning tasks, nine compatible dataset-model cells, 20 policies, and 1,800 on-policy runs. Fixed five-signal guidance outperforms Random and Uniform in every cell, while RCW and CSG further improve over Fixed in all nine cells. Under matched mutation and query budgets, RCW and CSG achieve 16.17% and 16.06% aggregate failure discovery, compared with 6.06% for the strongest neural-fuzzing baseline. Wall-clock evaluation further shows that TensorFuzz requires 1.11x the runtime of RCW while achieving only 0.45x its failure throughput. These results show that effective and efficient fuzzing of heterogeneous NSAI systems benefits from a common neural-symbolic risk interface with adaptive signal allocation.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.10292

Continual Instruction Tuning for LLM-based Management of Continuum Resources

Lanpei Li, Antonino Vaccarella, Vincenzo Lomonaco, Massimo Coppola

原文摘要

Large language models (LLMs) are increasingly explored for resource management across the Cloud-Edge continuum. However, embedding LLMs directly inside latency-sensitive control loops places inference latency, execution jitter, and hallucination risks on the critical path. This study argues for a different use of LLMs: a continually instruction-tuned intent compiler that runs once per management intent or declared context change, above a fast, deterministic tactical controller that handles all low-latency runtime actions. We formulate intent compilation as a continual instruction tuning problem, because management intents arrive sequentially and accumulate while infrastructure and workload contexts drift. Unlike recent LLM-based management frameworks, our design scores the model's outputs by execution, so that catastrophic forgetting can be measured in the executed objective of the underlying controller. On a continuum simulator with a 2B backbone and parameter-efficient adapters, preliminary results show that naive sequential tuning forgets earlier intents in the executed objective, that replay of stored examples nearly prevents this forgetting, and that the tuned compact compiler performs on par with simpler translators while being more robust than a grammar parser to rephrased intents.

cs.DC · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16207

Core nonemptiness for $2/3$-convex games

Keisuke Bando, Toshiyuki Hirai, Kenzo Imamura, Ryuhei Mizutani

原文摘要

We study the core of $2/3$-convex games, a class of transferable-utility (TU) games that generalizes convex games, which requires at least two of any three distinct coalitions satisfy the supermodular inequality. We show that every $2/3$-convex game admits a marginal worth vector in the core, and hence has a nonempty core. Our proof is constructive and yields a linear-time algorithm for finding such a core allocation. We also show that $2/3$-convexity does not imply permutational convexity in general, while the implication holds under an additional superadditivity condition on pairs of singletons. As an application, we introduce a TU model motivated by the OSCE's consensus-minus-two procedure and show that the resulting game is $2/3$-convex but need not be convex.

cs.GT · econ.TH

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16206

Turning Multi-Agent Communication into Token-Level Credit with On Policy Self Distillation

Zixu Wang, Yu Zhao, Xing Zheng, Bingbing Xu, Tian Xueyun, JiaTang Luo, Weiqiang Wang, Bowen Song 等 10 位作者

原文摘要

Reinforcement learning improves problem solving in large language model multi-agent systems. Existing approaches overlook communication knowledge or compress it into scalar rewards, leaving coarse credit with little guidance on which decisions each agent should reinforce or revise. OPSD4MAS converts future communication into fine-grained token-level credit through On Policy Self Distillation (OPSD). An external model extracts facts and assesses their trajectory relations for coarse-grained filtering, followed by fine-grained leave-one-out filtering of target-token influence against agent-wise outcome advantages. The fact-conditioned policy supplies token-level credit, combined with GRPO through a linearly annealed joint objective. Our method improves mathematical pass@4 over GRPO by up to 20.00 percentage points. Ablations support complete filtering, while frozen-history injection diagnostics support fact utility. Together, these results support combining agent-level outcome guidance with communication-derived token-level credit. Code is available at https://github.com/Camel-Prince/OPSD4MAS.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v8arXiv:2606.03161

OpenAgenet / OAN White Paper: Open Infrastructure for Trusted Agent Interconnection

Jinliang Xu

原文摘要

OpenAgenet (OAN) is an open infrastructure project for trusted Agent interconnection across independently operated networks. It addresses the verification of resource identity, governance state, discovery authorization, and freshness before interaction. OAN combines decentralized identifiers (DIDs), verifiable credentials (VCs), and on-chain infrastructure governance with a routing-aware did:oan registration and discovery model. Registrars verify onboarding evidence; the Root service accepts resource identity versions and publishes verifiable packages; authorized Discovery nodes return signed results within their governed scope. Root also governs the semantic tag tree shared by registration and discovery. Public DID Documents and credentials remain separate from local OAN Identity signing material and from controller authorization. Skill, Runtime, Sidecar, and Gateway integration paths allow existing Agents to adopt this identity model progressively, including through the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol, while retaining their native task and workflow semantics. Prototype evaluation and a public pilot support examination of the trust lifecycle and its operational bottlenecks. This white paper presents the architecture, governance model, ecosystem adoption paths, and standardization opportunities for OAN as a shared identity and trust infrastructure for heterogeneous Agent resources.

cs.MA · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13640

EyeQ: Exploring Corneal Reflection for Mobile Context Sensing

Jaewook Lee, Judith Amores, Donghoon Shin, Bo-Eun Choi, Jason Kim, Davin Win Kyi, Seok-Young Kim, Jon E. Froehlich 等 9 位作者

原文摘要

What if a single face-facing camera could sense your surroundings---through your eyes? We introduce EyeQ, which explores corneal reflection as a modality for context-aware mobile interaction. Traditional vision-based context sensing relies on world-facing cameras that demand explicit aiming or dual-camera setups; EyeQ instead shows that an off-the-shelf VLM can read scene context from corneal reflections captured with a smartphone camera. To recover usable detail from mobile corneal crops, EyeQ pairs on-device iris-guided capture with server-side AI enhancement, then leverages VLMs to extract scene types, names, and objects from warped, unfamiliar eye-image sequences. In an N=10 study, we characterize how capture conditions shape reflection fidelity, evaluate what VLMs detect, misclassify, and hallucinate, and demonstrate interaction opportunities, including detecting a steering wheel to distinguish drivers from passengers. This work reframes the eye in HCI---from a target for gaze estimation to a passive mirror of one's surroundings---and examines the privacy tensions it raises.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05618

When can multi-fragment 2D/3D registration be trusted? Calibrated certification and uncertainty-driven view planning for pelvic fracture fluoroscopy

Bowen Xiang

原文摘要

Registering a segmented CT to fluoroscopy can locate every fracture fragment but can guide surgery only when trusted. After each shot, our closed loop registers all fragments, bounds their errors with a calibrated sandwich covariance, flags implausible or ambiguous ones, and stops or plans the next view. On simulated fluoroscopy of 60 held-out fracture CTs with realistic image uncertainty, 95% bounds covered 83% of unflagged fragments and 91% of those within 5mm, but from the second view on 14% failed by over 5mm, a third with bounds of at most 2mm. A fragment's shift when a view was added separated tightly bounded failures best (exploratory; AUC 0.94, bound 0.81). Requiring that the newest view moved no fragment beyond its bound, preferred post hoc, lowered false stops at 2mm from 23% to 6% overall, with 345 instead of 362 correct ones but more trajectories uncertified; delaying the stop did not, and other signals at equal acceptance left 10-15% false. With a segmentation error shared by all views (median Dice 0.89), 21% of this rule's stops were false against 10% without it (15 cases; exploratory). Minimax planning certified sooner than the clinical protocol without the check (4.20 versus 4.72 shots, Holm p <0.001), but no criterion reached the pre-specified endpoint, a true 2mm error, significantly sooner, even in the true basin. On real fluoroscopy of five intact pelves no 2mm certificate was false. Trust takes evidence across views, not only a calibrated local bound.

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.00282

Constrained Bilevel Reinforcement Learning via Approximate Lower-Level Responses

Naman Saxena, Vaneet Aggarwal

原文摘要

We study constrained bilevel reinforcement learning in which the lower-level problem is a constrained Markov decision process with a generally parameterized policy. The lower-level solution map can be set-valued and need not be Lipschitz continuous in the upper-level variable, so standard hypergradient analyses do not apply. We therefore define the upper-level objective as the expected upper-level loss of the policy returned by a fixed stochastic lower-level algorithm, which we call the approximate response. This objective is well defined without uniqueness of the lower-level solution. To compute its gradient, we randomize the reward and constraint values used during lower-level training without changing their means, so that the upper-level variable affects training only through known probabilities. This yields an unbiased estimator of the upper-level objective's gradient that does not differentiate through the lower-level updates. We establish lower-level reward and feasibility guarantees, upper-level stationarity, and a total sample complexity of $\widetilde{\mathcal{O}}(δ_\ell^{-6}ε_o^{-2})$ over both levels, where $δ_\ell$ is the lower-level accuracy and $ε_o$ is the upper-level stationarity error. Setting $δ_\ell=ε_o=ε$ gives $\widetilde{\mathcal{O}}(ε^{-8})$.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16205

QVS-LIVO RiskMap: Health- and Information-Aware View Selection for Multi-Camera LIVO and Off-Road Risk Mapping

Zihan Pan, Jinghan Wang, Hang Li, Feng Cheng, Tianchen Liu, Gaoliang Peng

原文摘要

Multi-camera observations in off-road environments vary significantly in reliability, posing challenges when complementing degraded geometric constraints. Motivated by this, we propose Quality-aware View Selection Lidar-Vision Odometry Risk Mapping (QVS-LIVO RiskMap), which decouples visual processing for state estimation from that for semantic mapping. The front end evaluates camera observations through read-only pretracking on a shared lidar-inertial posterior, combining observation health with geometric complementarity. Channel health memory and switching hysteresis govern selection of at most one eligible view for direct photometric correction. The posterior is retained when no view is eligible or the correction fails acceptance. The mapping back end integrates semantic observations from all available views in a shared world grid. Vehicle-scale geometry constrains semantic risk corrections, while spatial and temporal evidence supports risk confirmation. The system also produces a local bird's-eye-view (BEV) risk map and a global 2.5D semantic-terrain risk map with obstacle instance voxels. On two field sequences, QVS-LIVO achieved horizontal root mean square errors of 0.910 m and 1.664 m. It reduced error by 43.3% relative to FAST-LIO2 on the degraded long-route sequence. Across 12 controlled camera-fault configurations involving dropout, black frames and 60 ms timestamp shifts, it maintained complete trajectories with errors of 1.54-2.23 m. When deployed onboard an NVIDIA Jetson AGX Orin, the system generates local and global maps at 9.94 Hz and 2.00 Hz, respectively, with a median local mapping latency of 32.4 ms. These runtime results demonstrate the real-time feasibility of selective visual state updates alongside multi-view semantic mapping under challenging off-road conditions. The code and related resources are available at: https://github.com/zihanPan-HIT/QVS-LIVO.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.15221

HarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems

Zhaoyuan Qiu, Sachith Seneviratne, Yu Xia, Saman Halgamuge

原文摘要

AI agents can evolve by using environment feedback to revise their harnesses, the software managing model inputs, tool use, memory, and execution, while keeping foundation models frozen. Existing studies focus on single agents, leaving joint harness evolution in multi-agent systems (MAS) underexplored. A single global optimizer faces growing context and workload as the system scales, while access restrictions may prevent sharing agent internals or system-wide feedback. We propose HarnessNet, a framework for hierarchical harness evolution in MAS. Its basic unit, Agentron, comprises an evolvable forward harness, a foundation model, and a local optimizer that can observe and modify only that harness. Agentrons form a layered graph through which environment feedback propagates backward, with upstream optimizers receiving node-specific feedback from the downstream layer. This decomposes joint harness evolution into smaller, coordinated tasks with separate contexts, reducing individual optimizers' context and workload while keeping access local. Optimizers need not access all agent trajectories or raw environment feedback. Experiments on AgentBench with multiple foundation models demonstrate the feasibility of training HarnessNet. Compared with a single global optimizer, HarnessNet achieves comparable performance gains while respecting local privacy and requiring fewer tokens and less time per optimizer call. A migration study shows that the evolved harnesses remain jointly effective after replacing the foundation models, suggesting transferability across models.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21895

SurgStreamer: proactive, request-driven monitoring of surgical video streams with structured state and a reusable reasoning layer

Bowen Xiang, Jon S. Heiselman

原文摘要

Surgical teams could benefit from assistants that watch the video for requested events, such as "tell me when the clip applier appears during Calot triangle dissection", and answer questions about what has already happened. We formalise this task as request-driven event detection with exact semantics and causal alert decisions, and present SurgStreamer. It separates procedure-specific perception, which maps the 1-FPS stream to a structured state over tools, actions and phases, from procedure-agnostic reasoning, which evaluates a formal monitoring condition over this state and an event memory, either with exact rules or with a small controller that addresses entities through pointers rather than names. We evaluate this monitoring core with gold conditions. On a pre-registered, sealed test set of 12 laparoscopic cholecystectomies, SurgStreamer reached a macro event-F1 of 0.598 over full procedures. On identical 75-s windows with only the window available to every method, it scored 0.677. The tested configurations of the streaming vision-language model OneStreamer-4B, including ones fine-tuned on SurgStreamer's state or on video with an adapted vision encoder, scored at most 0.540 (Holm-adjusted p <= 0.012). At three external centres, re-training only perception raised performance on held-out procedures from 0.239 to 0.470. On a new procedure, pituitary surgery, controller weights trained on cholecystectomy matched an in-domain controller on oracle state (0.947 vs 0.938). On predicted state they missed the pre-registered non-inferiority margin (-0.024 [-0.040, -0.008]) unless the alert thresholds were re-calibrated. The results support separating perception from reasoning and point to perception under distribution shift as the main limitation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19423

Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training

Anshu Raina, Peyman Razaghi, Yuankai Chen, Cheng Yao, Yao Fu, Vidushi Goyal, Devang Patel, Wen Xie 等 10 位作者

原文摘要

Configuring distributed LLM training across hundreds of GPUs requires jointly optimizing tensor, pipeline, expert, context, and data parallelism alongside micro-batch size, recomputation, sharding, and overlap, a combinatorial space where memory legality can hinge on a single byte of activation accounting. Today, teams navigate it through expensive multi-node trial and error, burning GPU-hours on crashes and suboptimal recipes. We present Primus Projection, which closes this loop with two coupled components. First, a hybrid projection engine that measures what it can and simulates what it cannot: GPU microbenchmarks on as few as a single device capture compute kernels and any in-scope communication; a sub-node downscale-measure-upscale workflow analytically restores out-of-scope collectives, FSDP overlap, and the full pipeline schedule at the multi-node target. A pure simulation mode runs entirely on CPU, enabling pre-silicon planning without any GPU. Second, the Primus Tuning Agent treats the projection engine as a fast scoring oracle (seconds per candidate instead of tens of minutes) and uses it to drive an intelligent search over the configuration space. A deterministic seed planner sweeps single axes; an LLM investigation loop proposes cross-axis combinations the planner cannot reach, with an analytic memory pre-filter that rejects infeasible candidates before any GPU time is spent. Validated on Llama 3.1 (dense) and Mixtral 8x22B (MoE) across two GPU generations, all projections stay within ~10% of measured multi-node throughput, and fidelity holds at production scale: 4.7% error on DeepSeek V3 671B at 1,024 GPUs and 1.9% on Qwen3-235B-A22B at 256 GPUs. In a Mixtral 8x22B case study, the Primus Tuning Agent discovers a configuration delivering +27% throughput over the published 4-node BF16 reference at the same scale, in under 30 minutes of single-node exploration.

cs.AI · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16202

An Agentic Python Framework for LLM-Driven UVM Testbench Synthesis with Closed-Loop Compiler Feedback

Vikash Kumar

原文摘要

Protocol bridge verification is a persistent bottleneck in SoC development: for every new bridge instance, engineers hand-craft a full UVM testbench from scratch, consuming 40--100 engineering hours of bring-up effort. We present an agentic Python framework that generates complete, protocol-aware UVM testbenches for arbitrary IP and subsystem configurations from a structured Verification Manifest---a JSON specification encoding interface topology, protocol semantics, and coverage intent. The framework employs a dual closed-loop architecture: an inner compiler feedback loop that injects categorised UVM diagnostics rather than raw tool output, and an outer coverage loop driven by intent-gap analysis against the Manifest. Validated on the AMBA AHB-to-APB bridge---a representative production protocol bridge with a 7-state control FSM, two-phase APB handshake, and non-trivial burst serialisation logic---the framework achieves 96.8% functional coverage, a $4.7\times$ bring-up speedup, and a Protocol Verification Completeness score of 0.87, with only 2.1% of the generated 4,623-line testbench requiring human correction. The generated scoreboard independently identified a critical RTL functional fault that the manual baseline missed entirely. Generalisation across four protocol families confirms that speedup increases with protocol complexity, ranging from $3.1\times$ (APB Subordinate) to $6.1\times$ (CHI Subordinate).

cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16201

An Extensive Evaluation of Machine Learning and Deep Learning Approaches for Sentiment Analysis: Insights from Amazon Customer Reviews

Muhammed Ihsan Corak, Cuiyuan Wang, Xiaowen Zhang

原文摘要

The rapid growth of user-generated text on online platforms creates opportunities for understanding customer opinion and supporting data-driven decisions. This study compares five traditional machine learning classifiers (Random Forest, Logistic Regression, Bernoulli Naive Bayes, Linear Support Vector Classifier, and Multinomial Naive Bayes) and six deep learning architectures (LSTM, Bi-LSTM, GRU, RNN, Attention, and FastText) for three-class sentiment classification of 568,454 Amazon Fine Food Reviews. Machine learning models used Bag-of-Words (BoW) and TF-IDF features limited to 5,000 terms; deep learning models used learned embeddings over a 20,000-word vocabulary. Models were evaluated on the original imbalanced data and on a random-oversampled balanced set of 1,070,469 reviews, using accuracy, macro and per-class precision, recall, and F1-score, and training time. On the balanced set, Random Forest with BoW achieved 99\% accuracy and the Attention model achieved 98.7\%. Linear SVC reached 89\% accuracy in 1 min 15 s, and Logistic Regression reached 90\% in 3 min 37 s. On the imbalanced set, the Attention model achieved 97.8\% accuracy, and FastText achieved 95.3\% in 3 min 26 s, while the RNN reached 91.9\% with a neutral-class F1-score of 0.08. Deep learning models were more stable across class distributions, whereas the best classical model depended on balancing. We also report a user-facing application that applies the trained Random Forest and Attention models to new review text. Because the labels are derived from star ratings, the results measure agreement with rating-derived sentiment classes rather than annotated sentiment; the dataset is a single source, and the findings should be read with that limitation in mind.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.16102

Where Polanyi Meets Galois: Tacit Knowing as an Algebro-Deterministic Operation, Toward Re-rooting AI-Generated Science

Hiroyuki Chuma, Kanji Otsuka, Yoichi Sato, Kodo Yamauchi

原文摘要

Tacit knowing is an operating regime of one memory substrate, not a separate kind of content. In VaCoAl (CASRAM, PyVaCoAl), an algebro-deterministic hyperdimensional memory over GF(2), one DAG of bound records is read in two ways. With every collision repaired (Rescue), the trace is exactly auditable but no path outranks another; with collisions tolerated (Don't Care), a path-integrated confidence $CR_2$ ranks direct over circuitous routes without being stated. This audit-valuation complementarity formalizes Polanyi's claim that tacit and explicit knowledge are not sharply divided. (1) Polanyi's from-to structure reads as non-commutative binding. (2) Full explication destroys the valuation (Lemma 1), as with Polanyi's pianist; tacitness is contingent or regime-bound, placing Polanyi, Nonaka, Tsoukas and Collins on one substrate. (3) The same lemma answers the popular dual-process moral ("train slow System 2 to tame fast System 1"), which mirrors the two-store reading of knowledge Polanyi rejected; Mercier and Sperber's reply, that reasoning is intuitive inference about reasons, matches his. (4) To know a large DAG is to cull it. Simon answered Polanyi's Meno argument: a test and a generator suffice. The AlphaGo-to-AlphaProof lineage builds that answer, yet a checker audits an endpoint and values no path: the tacit is relocated, not removed, and results are verified yet rootless. (5) Collins's three tacit knowledges map to bound structure, substrate valuation and value binding. (6) Our mentor-student DAG charts tacit transmission. (7) AI-solved problems with few follow-ups (Tao) are rootless explicit knowledge, re-rooted when LLMs generate, VaCoAl keeps the trace and the community values. (8) On Mercier and Sperber's theory an LLM is a sophist; truth-seeking lies in collective tacit knowing, Polanyi's conviviality, which we conjecture extends beyond humans. Testable predictions follow.

cs.NE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16198

"We Are the Dirty Hands": Reimagining an Ambient Intelligence Care System with Professional Caregivers

Andrea Green, Jane L. E, Sneha Jain, Parker Ruth, Matthew Jörke, Sarah L. Billington, James Landay, Andrea Cuadra

原文摘要

While ambient assisted living (AAL) sensing systems offer promise for aging in place, their impacts on professional caregivers are overlooked. We conducted two collective speculation workshops (N=24) with personal health aides (PHAs, n=18) and certified nursing assistants (CNAs, n=6) to examine the ethical implications and value tensions of a care system with ambient intelligence (CSAI), our conceptualization of an AAL. Participants anticipated different implications across care roles and settings. PHAs anticipated additional responsibilities from CSAI, including learning how it operates and supporting older adults in its use. CNAs, who had greater access to workplace resources and protections, similarly noted that CSAI could create additional work. Participants also identified tensions around visibility, as sensing could support caregivers by documenting difficult working conditions while simultaneously making their behavior more vulnerable to misinterpretation. Through their modifications and surfaced discussions, we propose design guidelines for AAL systems, such as integrating context-aware sensing.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16197

Mechanical behaviour of the intact brain-skull interface under tension, compression and shear

Sajjad Arzemanzadeh, Karol Miller, Ron Kikinis, Adam Wittek

原文摘要

The brain-skull interface governs the boundary/contact conditions of the brain in computational head models, yet its mechanical behaviour remains poorly characterised experimentally. We characterised the brain-skull interface by testing brain-skull complex samples (skull bone, meninges and brain tissue in their native arrangement) excised from 19 sheep cadaver heads under uniaxial tension and compression at strain rates of 0.002, 0.02 and 0.2 s$^{-1}$ and simple shear at 0.02 s$^{-1}$. Histology confirmed that the meningeal layers remained intact and attached to the brain and skull after excision. We acquired magnetic resonance images of every sample, reconstructed their geometry and developed a sample-specific finite element model of each experiment. We represented the brain tissue using a second-order Ogden hyperelastic model and the brain-skull interface using a cohesive zone model. Using inverse finite element analysis, we first calibrated the brain tissue parameters for each subject. Keeping these parameters fixed, we then identified the interface parameters from the corresponding brain-skull complex specimen. The brain tissue shear modulus increased with strain rate from 0.94 $\pm$ 0.16 kPa at 0.002 s$^{-1}$ to 1.60 $\pm$ 0.30 kPa at 0.2 s$^{-1}$. In contrast, the interface properties (maximum normal traction 2.35 $\pm$ 0.81 kPa, maximum tangential traction 4.14 $\pm$ 0.97 kPa, and fracture energy 0.35 $\pm$ 0.14 N m$^{-1}$) showed no significant dependence on strain rate or loading mode. In all samples, failure occurred within the subarachnoid space. Neither a rigid brain-skull connection nor frictionless sliding contact with separation reproduced the measured mechanical response or failure mode of the brain-skull interface. Our experimentally derived cohesive zone model provides a realistic representation of the brain-skull interface for computational head models.

cs.CE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.12048

Evaluating AI Agents for Industrial-Level Chip Floorplanning: A Framework and Empirical Study

Nova-Silicon Team

原文摘要

Floorplanning is an early step in turning a logical circuit into a physical chip: it positions large circuit modules and constrains the space available for subsequent logic placement and wiring. These decisions affect congestion and timing, but their physical consequences emerge through downstream implementation. For AI agents, the task requires translating engineering constraints into spatial decisions whose quality cannot be judged by successful tool execution alone. We present an evaluation framework that connects submitted layouts to a prescribed downstream implementation flow, checks geometric validity, measures physical quality, and reports resource use. On one industrial design whose 99 macros occupy approximately 71\% of a fixed L-shaped layout region, we compare our specialized system Nova-SI with general-purpose engineering agents and agents restricted to file access, obtaining complete physical measurements for 39 layout outputs. Under our 0--100 composite quality score, Nova-SI achieves a mean of 57.97 without online feedback from the prescribed power-grid and placement evaluation, compared with 31.17 for the strongest measured general-purpose configuration, GPT-6 Astra. The difference is supported by raw congestion and timing measurements and persists across scoring-sensitivity checks. Layout-level analysis further shows that similar global utilization can conceal substantially different local congestion. Together, the protocol and case study assess agents through the physical consequences of their delivered layouts and provide evidence about system performance on this industrial instance.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16194

GA-HEL: A Knowledge-Driven Expert System for Open-Set, Granularity-Aware Hierarchical Event Linking in Social Media

Shengqi Zhou, Zhigang Liu, Wenxiang Zhu, Zhentao Cang

原文摘要

Social-media event intelligence requires a system to determine whether a post belongs to a registered event space, identify the supported hierarchy path, and stop at the deepest level warranted by the evidence. We formulate this requirement as open-set hierarchical event linking over frozen Storyline--Episode--Atomic Event Cards. GA-HEL implements a three-stage, knowledge-driven expert-system funnel: open-set scope control limits candidate exposure before hierarchical reasoning, hierarchy-aware decoders select a supported path and depth, and deterministic legal-path validation checks each record before database-compatible use. A 200-post Pilot supports task and mechanism development, while a separately annotated 497-post Expansion set evaluates transfer across four held-out Storylines and a hard-negative pool. Method implementations, prompts, Event Cards, candidate order, and six systems' predictions were frozen before Expansion Gold annotation. The resulting evaluation combines end-to-end hierarchical matching, open-set rejection, structural validation, ablation, cross-Storyline analysis, and an independent expert reannotation of a fixed audited subset. GA-HEL remains competitive with strong global full-card reasoning while providing explicit scope control and hierarchy-valid structured records. Error analysis identifies unsupported descent from Episode-level evidence to Atomic Events as the dominant remaining challenge. The task definition, frozen resource, and auditable protocol provide a reproducible foundation for scope-aware event intelligence. Code, Event Cards, labels, frozen predictions, and evaluation scripts are available at https://github.com/SHbiao/GAHEL_release.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.13577

Lower Bounds for Testing Directed Acyclicity in the Unidirectional Bounded-Degree Model

Yuichi Yoshida

原文摘要

We study property testing of directed acyclicity in the unidirectional bounded-degree oracle model, where a query to a vertex reveals its outgoing neighbors. We prove that there exist absolute constants $d_0\in\mathbb{N}$ and $\varepsilon>0$ such that for every constant $d\ge d_0$, any one-sided $\varepsilon$-tester for acyclicity on $n$-vertex digraphs of maximum outdegree at most $d$ requires $\widetildeΩ(n^{2/3})$ queries. This improves the previous $\widetildeΩ(n^{5/9})$ lower bound for one-sided testing of acyclicity in the same model. We also prove that, under the same degree assumption, any two-sided $\varepsilon$-tester requires $Ω(\sqrt n)$ queries, improving the previous $Ω(n^{1/3})$ lower bound. We further prove an $Ω(n)$ lower bound for tolerant testing for some absolute constant outdegree bound $d$ by reduction from bounded-degree $3$-colorability.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16192

Weak Composition Theorems for Randomized Communication

Pachara Sawettamalya

原文摘要

We present two results towards the randomized communication complexity of composed functions. Our contributions include: - \textbf{Lifting-by-Influence.} For any binary function $f$ and any outer gadget $g:\{0,1\}^n \rightarrow \mathbb{R}$, we show that up to additive terms, computing $g \circ f^n$ information-theoretically requires solving at least $\operatorname{Inf}(g)$ instances of $f$, where $\operatorname{Inf}(g)$ denotes the total influence of $g$. To our knowledge, this is the first composition lower bound for randomized communication that applies to \emph{every} choice of $f$ and $g$. - \textbf{Symmetric Composition.} When $g$ is a symmetric function with a value transition at Hamming weight $d$, we strengthen the number of required instances to $\min\{d,n+1-d\}$. As an implication, this recovers the previously known direct-sum theorem, XOR lemma, and majority lemma. Our proofs develop novel and distinctive usages of the classical \emph{input embedding} argument of Barak, Braverman, Chen, and Rao (SICOMP 2013). As an additional ingredient, we introduce a new public sampling framework that avoids auxiliary communication during the recovery phase of the protocol, ultimately reducing the extra cost from $O(n)$ bits of information to just $O(1)$.

cs.CC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03093

An Experimental Study of Hybrid Retrieval, NLI and Open-source LLMs for Scientific Claim Verification

Lakshmi Narayanan P, Narenkarthik K, Naren Kumar C

原文摘要

Claim verification is the task of deciding whether a source text supports a claim, contradicts it, or lacks sufficient evidence. Natural Language Inference (NLI) models can judge whether a single sentence supports or contradicts a claim, but they struggle when the evidence is spread across several sentences. Conversely, Large Language Models (LLMs) can reason across sentences, but may draw conclusions the text does not support. In this experimental study, we test whether a conservative fusion rule, labeling a claim Support or Contradict only when both models agree and defaulting to Not Enough Evidence otherwise, combines their strengths. We construct a benchmark of 2,035 claims from SciFact and CliniFact (858 Support, 152 Contradict, 1,025 Not Enough Evidence). We retrieve the top five most relevant sentences per claim using a hybrid of semantic and lexical (BM25) retrieval, which slightly outperforms either method alone. These retrieved sentences are then provided to every verification model. We then compare a DeBERTa-v3-base-mnli-fever-anli NLI baseline against Llama 3.1 8B, Qwen3 8B, and Mistral 7B. Qwen3 achieves the highest Macro-F1 (0.7937), followed by Llama 3.1 (0.7204) and Mistral (0.6388), while the NLI baseline reaches only 0.3157. Contrary to our hypothesis, fusion degrades performance, dropping Macro-F1 to 0.7392, 0.6730, and 0.5991, respectively. To understand this decline, we analyze agreements, disagreements, and common error patterns between the NLI and LLM verifiers. Our error analysis shows that NLI and LLM disagreements often result in correct LLM predictions being overturned.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16190

When can an interpretability claim be trusted? Stability and certification of feature dimensionality, ridge leverage and the feature recovery rate

Ali Tajfar, Behnam Tajfar

原文摘要

Interpretability tools increasingly support safety claims, such as that a sparse dictionary has recovered most of a model's features. Such claims rest on measurements with arbitrary thresholds, and small changes to the model weights, a different training seed or a different threshold can reverse them. We study two widely used measurements in toy models and synthetic dictionaries with known ground truth: the feature dimensionality of Elhage et al. and the feature recovery rate (FRR) with a cosine threshold. We characterize exactly where feature dimensionality is discontinuous and replace it with a regularized (ridge) version with sharp stability bounds: the ridge leverage score changes by at most $3\sqrt{3}/(8\sqrtλ)$ times the operator-norm change of the weights, and this constant cannot be improved; for the regularized dimensionality the stability constant grows like $ρ/4$ in the scale $ρ=R/\sqrtλ$, with explicit upper and lower bounds. We also prove certificates stating when a claim "feature $i$ is recovered" must survive a given change of the dictionary. The certificates held in every test and are informative for small perturbations, but certify no feature non-trivially across independently trained dictionaries; at the standard threshold, whether a feature counts as recovered changed with the training seed for 55 percent of features. Experiments use toy models only. The proofs, code and text were drafted by AI agents directed by the authors and checked by an independent blind AI reviewer and by numerical tests; the authors take full responsibility for the content.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16189

SBA: A Practical Memory Performance Denial-of-Service Attack

Amin Mamandipoor, Connor Rudy Sullivan, Cole Ridge Strickler, Mohammad Alian, Heechul Yun

原文摘要

Modern cloud platforms use memory bandwidth regulation to isolate co-located workloads, implicitly assuming that high aggregate bandwidth consumption implies high interference. We show that this assumption is fundamentally flawed. We present the Single-Bank Attack (SBA), a practical memory denial-of-service attack that induces extreme cross-core slowdowns---over three orders of magnitude on memory-intensive victims (e.g., LLM decode)---while consuming less than 1 GB/s of memory bandwidth. SBA exploits the gap between peak and worst-case DRAM throughput by concentrating memory write operations on a single bank, forcing persistent row conflicts and frequent write draining, thereby collapsing the memory service rate. Across Intel and AMD systems, including DDR5- and HBM2E-based platforms, SBA consistently causes orders-of-magnitude slowdowns under realistic multi-tenant settings, revealing a fundamental limitation of bandwidth-centric memory isolation. We further show that this localized contention propagates system-wide: single-bank congestion saturates memory controller queues and induces back-pressure into shared cache and coherence structures, ultimately throttling memory request injection across cores. Consequently, severe interference can arise even at low aggregate bandwidth, evading existing bandwidth-based QoS mechanisms such as Intel RDT MBA. To address this limitation, we propose IQ-MemGuard, a lightweight, software-based memory bandwidth regulator that detects sustained contention using microarchitectural signals and selectively throttles pathological request injection. Our implementation on an Intel platform effectively mitigates SBA, whereas Intel's hardware QoS mechanism fails completely.

cs.AR

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arXiv ↗PDF ↗