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版本更新 · v2arXiv:2610.26676

Which Model for Which Role? Cost-Efficient Pareto Recommendation for LLM Agentic Workflows

Qian Xie, Wenyue Hua, Yueli He, Sripad Karne, Armaan Agrawal, Nikos Pagonas, Eugene Wu, Kostis Kaffes 等 10 位作者

原文摘要

LLM agentic workflows increasingly employ different models for specialized roles. Users may prefer different trade-offs between task quality and deployment cost, sometimes also considering latency. Finding good assignments incurs a separate search cost from evaluating multiple candidates. We introduce Cost-Coupled Gittins (CC-Gittins), a multi-objective extension of single-objective cost-aware Gittins search for Pareto recommendation. It exploits the coupling between evaluation and deployment costs: assignments that cost more to deploy also cost more to evaluate. The search alternates between seeking higher quality and lower deployment cost, reusing each evaluation to inform both searches. After each batch, CC-Gittins recommends assignments that are nondominated under uncertainty-adjusted estimates of quality and deployment cost. We evaluate CC-Gittins on eight quality--cost matrices from workflows with two to five roles and up to 1,681 candidate assignments. We also evaluate the method with latency as a third objective. Across the eight benchmarks, CC-Gittins recovers a substantial fraction of the true Pareto frontier at roughly 10% of the exhaustive-evaluation cost, and recovers most of the frontier with few false positives by 30%.

cs.LG · cs.CL · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.19899

Trigger Timing, Deadline Readiness, and Event-Aligned Accounting for Dynamic Ad Insertion

Prashant Chaudhary, Kapil Khandelwal

原文摘要

An ad break in a live stream is a hard deadline. Once a cue arrives, the ad path has seconds to produce a state the player can use, and starting that work earlier buys time while giving the prepared state longer to expire before the break. Comparing trigger policies means scoring which of these happened; the records easiest to collect do not. We separate trigger, reach, readiness, playback, billability and measurement in an observed-event ledger, fix the reference-deadline rule before candidates are assigned, and account for contribution over every planned break-session pair, reached or not. Readiness means the declared state is valid at that deadline. We prove when scoring completion by the deadline instead reverses a policy comparison: for monotone playback with a shared latency law, a fixed state lifetime and common coefficients, the reversal occurs when the earlier policy's extra trigger cost falls between its validity gain and its completion gain. A synthetic study of 45,000 scripted sessions matches this condition on monotone paths, a check of its generator, and, outside its assumptions, finds the same reversal in mixtures with restarts, seeks, late joins and pauses. Playhead summaries misclassify up to 41% of reach indicators yet reverse no comparison in the study: maximum-position errors cancel exactly, and terminal-position errors change only magnitudes. Completion scoring reverses the comparison in three of nine short-lifetime conditions, all at the one latency setting where the proved condition holds. The evidence is synthetic by design; code, seeds and event histories are public.

cs.MM · cs.NI · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39408

YOLOF-MLD: A Multi-Level Distillation Framework for Efficient Object Detection

Xiaolong Wu, Liming Zheng, Zitong Liu, Yue Zheng

原文摘要

Efficient object detection on resource-constrained edge devices is essential for safety-critical applications such as fire-smoke monitoring. However, current lightweight frameworks, such as YOLO26, still rely on the parameter-dense multi-branch Feature Pyramid Network (FPN), preventing full exploitation of their lightweight advantage. Replacing FPN with YOLOF's single-input-single-output (SiSo) neck is a promising solution but causes substantial accuracy loss. To alleviate this, we propose YOLOF-MLD, a CPU-efficient multi-level distillation (MLD) framework that combines a YOLO26n backbone, a SiSo neck, and MLD. It aligns features at the backbone via sliced-semantics matching, neck with receptive-field matching, and response levels. On D-Fire and Annotated Fire-Smoke (AFS) datasets, YOLOF-MLD reduces the parameter count of YOLO26n by 35.7\% and achieves a 62.3\% speedup on Raspberry Pi 5, attaining 71.6% and 59.9% mAP@50, respectively, compared with 75.5% and 60.0% for YOLO26n. These results demonstrate a competitive accuracy--latency trade-off for edge deployment. Our code is available at https://github.com/XiaolongWU111/YOLOF---MLD/blob/main/README.md.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39406

QSoftCore: A Conceptual Foundation for Quantum Software Engineering

Tao Yue, Yuechen Li, Man Zhang

原文摘要

Quantum software engineering (QSE) requires understanding application requirements, quantum programs and algorithms, quantum computation paradigms, executions, and deployment. However, relevant quantum computing (QC) concepts are often defined across physical, mathematical, and paradigm-specific levels of abstraction, making it difficult for the QSE community and hence proposed methods to refer to them consistently. To this end, we present QSoftCore, a conceptual model that reorganizes established QC concepts from a software engineering perspective and connects quantum applications, computational problems, algorithms, configurations, programs, and paradigm-specific computation specifications with state evolution, execution results, and backends. Our assessment against QSE literature across key software engineering activities indicates that QSoftCore provides shared QC referents for quantum computations, executions, deployment configurations, backends, and measurement evidence across the QSE lifecycle.

cs.SE · quant-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39403

SymKL: A Unified Representation-Space Measure of Signal Degradation

Dhananjaya Jayasundara, Sudarshan Rajagopalan, Vishal M. Patel

原文摘要

Quantifying degradation between a pristine signal and its degraded observation is fundamental to signal processing, yet existing full-reference measures are inherently modality-specific. We introduce SymKL, a unified full-reference degradation measure based on implicit neural representations (INRs). An INR is fitted once to the pristine reference and characterized by its diagonal Fisher information, which captures the local information geometry of the learned representation. Evaluating the Fisher information with the pristine reference and a degraded observation yields two parameter-sensitivity profiles whose symmetric KL divergence defines the degradation score. The proposed formulation requires only the reference and degraded observation, without knowledge of the corruption process or degradation severity, making it applicable across images, audio, and 3D data. We further derive an exact closed-form expression for the diagonal Fisher of SIREN, enabling efficient computation. Experiments on image, speech, and volumetric 3D benchmarks demonstrate that SymKL accurately tracks degradation severity, remains monotonic where conventional metrics saturate or fail, and provides a unified modality-agnostic measure of signal degradation. Code will be made publicly available after the review process.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13406

Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement

Hongyao Tang, Yi Ma, Pengyi Li, Yifu Yuan

原文摘要

When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these emerging instances exists. Its counterpart in the classical realm, iterative policy improvement, is characterized by generalized policy iteration (GPI), a framework of broad applicability with well-understood theoretical properties, but only where the update principle and the evaluation base lie outside the agent. In this paper, we propose Generalized Agent Iteration (GAI), a formal framework that describes iterative policy improvement and RSI as two cases of a single learning paradigm. GAI defines the agent as a configuration of modifiable components within a system and models the learning process as a cycle of agent evaluation and agent improvement. Two pivotal dials then distinguish the instances, in particular the systems that self-identify as RSI in recent work: whether the improving mechanism is part of the agent and whether the standard it is measured against is grounded outside it. The former dial delineates the boundary between GPI and RSI, and the latter determines a system's polarity as anchored, goal drift, or fully self-referential. Moreover, we use these coordinates to place existing systems on the same two axes and make the defects of recursive self-improvement statable one condition at a time. We see this paper as a first step toward exploring a formal characterization of RSI that rests on the classical account, makes existing systems comparable, and provides a principled basis for analyzing and designing new ones.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.06840

Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces

Debjyoti Saha Roy

原文摘要

Reasoning-trained language models can select, zero-shot, a small set of relevant labels from thousands to hundreds of thousands of candidates. We ask how they do it mechanistically, and whether the mechanism can be distilled. We treat each decision as a token-level event scored by the model's own decision margin: the token that picks a coarse region of the label space, the tokens that pick a label within it, and the token where the output departs from a near-miss named earlier in the reasoning. Attribution, exact mean-ablation, knock-in into another example's context and a null calibration give individual attention heads causal standing. On clinical coding of MIMIC-IV discharge summaries, with all 5,651 candidate codes in context, a small, global, phase-structured set of heads is necessary and sufficient on essentially every summary; distinct head families read the candidate region and the earlier near-miss, and blocking what they read removes their effect; for mentions elicitable in the reasoning, a disjoint mid-layer set makes the region elicitable several tokens before the code. The structure recurs in a second reasoner. We introduce MISTILL, which, unlike chain-of-thought distillation, also supervises the student's pooled attention at these decision events. Read on heads found after training, it nearly doubles the causal recovery of the contrastive decision in a cross-family student and adds a small, seed-stable gain in one that already carries most of it, with no detected task difference at matched precision; under matched selection it exceeds a frozen random-head pool on both recovery and task score. A pre-registered evidence-flip test detected no improvement in evidence-dependent decisions through the tested routes.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39399

ACD-GS: Asymmetric Curvature-aware Densification for 3D Gaussian Splatting

Houqiang Zhong, Tianchi Zhu, Qiang Hu, Xiaoyun Zhang, Li Song, Wenjun Zhang

原文摘要

3D Gaussian Splatting (3DGS) has emerged as a powerful paradigm for real-time photorealistic novel view synthesis. However, its training-time densification remains largely heuristic, often resulting in redundant Gaussian proliferation and inaccurate geometric reconstruction. Existing methods predominantly rely on first-order screen-space gradients to trigger cloning and splitting, which fail to distinguish true geometric discontinuities from high-frequency textures or noisy residuals. To address this limitation, we propose ACD-GS, an Asymmetric Curvature-aware Densification framework for compact and geometry-aware 3D Gaussian optimization. Our key insight is that second-order depth responses provide a more reliable structural prior for density control, enabling asymmetric regulation of cloning and splitting to better capture geometric discontinuities. Building on this, we introduce a multi-view photometric consistency constraint to suppress transient single-view errors, together with a capacity-aware adaptive regulation strategy that balances densification and pruning during training. Unlike post-hoc compression or quantization-based approaches, ACD-GS directly prevents redundant Gaussian generation during training, yielding inherently compact representations. Extensive experiments demonstrate that our method reduces nearly 60% of Gaussian count and storage while maintaining competitive rendering fidelity, consistently outperforming existing methods across multiple datasets. Our code is available in https://waveviewer.github.io/ACD-GS/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39398

Robustness of Chatbot LLM Judgments of Presidential Actions and Hypothetical Reforms

Oleg Vasilyev

原文摘要

Chatbots are increasingly consulted about public affairs, and their judgments of policies are useful only if they are robust. We measure this robustness in the setting of an ordinary user, through consumer chat interfaces. For 30 days, five chatbot configurations (Mistral, Gemini Flash, Claude Sonnet, and Claude Opus at two effort settings) were asked twice a day to score the long-term effect on the United States of 40 actions of the last four presidential administrations and of 15 hypothetical reforms, each with a confidence and a short comment. The judgments are robust: all configurations agree on the sign and the ranking of the administrations' mean scores, and two different configurations asked on the same day give opposite signs to the same action in about 4% of cases. Responses from different days differ more than same-day responses, but the difference grows slowly over the month, except where the service itself changed. We identify three such changes, which shifted the judgments while leaving little or no trace in the interface. Treating the comments as a retrieval task, we find them specific to each item and consistent across runs and chatbots, with lexical and embedding similarities agreeing. We release the dataset of 16,500 judgments.

cs.CL · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.17940

Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere

Shogo Iwazaki

原文摘要

We study an algorithm-independent, worst-case lower bound for the Gaussian process (GP) bandit problem in the frequentist setting, where the reward function is fixed and has a bounded norm in the known reproducing kernel Hilbert space (RKHS). Specifically, we focus on the squared exponential (SE) kernel, one of the most widely used kernel functions in GP bandits. One of the remaining open questions for this problem is the gap in the \emph{dimension-dependent} logarithmic factors between upper and lower bounds. This paper partially resolves this open question under a hyperspherical input domain. We show that any algorithm suffers $Ω(\sqrt{T (\ln T)^{d} (\ln \ln T)^{-d}})$ cumulative regret, where $T$ and $d$ represent the total number of steps and the dimension of the hyperspherical domain, respectively. Regarding the simple regret, we show that any algorithm requires $Ω(ε^{-2}(\ln \frac{1}ε)^d (\ln \ln \frac{1}ε)^{-d})$ time steps to find an $ε$-optimal point. We also provide the improved $O((\ln T)^{d+1}(\ln \ln T)^{-d})$ upper bound on the maximum information gain for the SE kernel. Our results guarantee the optimality of the existing best algorithm up to \emph{dimension-independent} logarithmic factors under a hyperspherical input domain.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39396

CASE: Concept-Aware Segmentation from Positive and Negative Visual Exemplars

Yi-Jen Tsai, Yen-Yu Lin, Chien-Yao Wang

原文摘要

Few-shot semantic segmentation typically identifies target objects based on positive support examples of a single semantic class. In practice, however, positive cues alone are often underspecified: for instance, "red cars" may be confused with cars of all colors, "vehicles" may include cars that should be excluded, and "material-defined objects" may span multiple categories. Fulfilling precise requests requires positive exemplars to provide inclusion evidence and negative exemplars to act as intent-defining constraints for exclusion and disambiguation. We formulate this setting as Specified Visual Prompt Segmentation (SVPS), where the target concept is specified by both positive and negative visual prompts. In this work, visual exemplars refer to support-image regions specified by bounding-box prompts. To study SVPS, we introduce CASE (Concept-Aware Segmentation from Positive and Negative Visual Exemplars), a benchmark built on COCO and PACO. CASE encompasses six category- and attribute-level task families: exclusion, isolation, union, attribute exclusion, attribute isolation, and attribute union. We evaluate SAM 3 under this setting and find that, despite its powerful positive-only few-shot segmentation capability, frozen SAM 3 does not consistently treat negative visual prompts as exclusion evidence. To address this, we present Concept Prompt Adaptation (CPA), a conservative LoRA-based adaptation baseline that improves positive-negative prompt interpretation on CASE while remaining competitive under positive-only few-shot segmentation. Code is available at https://github.com/WongKinYiu/FSS-CASE.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39395

OPERA: OPamp Design by an Expert-Taught Reasoning Agent, from Specifications to Layout

Vishal Saxena, Soumyajit Mandal

原文摘要

Analog flows from specifications to a signed-off layout rarely run without manual iteration. Recent large language model (LLM) agents size circuits with optimizer loops over the device dimensions and stop at the schematic. We present OPERA (OPamp design by an Expert-taught Reasoning Agent), in which a general-purpose LLM agent designs CMOS opamps from specifications to post-layout sign-off with open-source tools. First, the designer teaches the agent the gm/ID method: map the specifications to transconductances and minimum currents, choose gm/ID per device role from characterized lookup tables, and size from equations, leaving a few knobs to search and an explainable design. Second, the agent learns the gLayout framework and taught layout practice, and writes a reusable layout generator that closes the layout and extraction loop at designer-approved gates. In SKY130, the agent designed a shared constant-gm bias and six opamps (5T, folded-cascode and telescopic OTAs; Miller, folded-cascode and telescopic two-stage) for fun >= 50 MHz, PM >= 60 degrees, SR 25 V/μs into 1 pF and a minimum dc gain of 30 to 60 dB. All six layouts are DRC-clean and LVS-matched, and their extracted netlists meet every target at all 15 PVT corners ( fun >= 53.7 MHz, PM >= 62.1 degrees, SR >= 27.9 V/μs). The benchmark netlists are released at https://github.com/AMPICLab/AMPICBench.

cs.AR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.17897

The Maximum Nonlinearity of 8-variable Balanced Boolean Functions is 116

Yueying Lou, Youle Xu, Qichun Wang

原文摘要

Does there exist an 8-variable balanced Boolean function with nonlinearity 118? This is a famous and long-standing open problem in Boolean functions. In this paper, we give a negative answer to this open problem, and prove that the maximum nonlinearity of balanced Boolean functions in 8 variables is 116, establishing the 8-variable instance of Dobbertin's conjectured recursion for the minimum Walsh peak of balanced functions. Equivalently, the covering radius of the Reed--Muller code RM(1,8) in 0-resilient Boolean functions is 116. We deduce the result by obtaining a quadratic form of polar rank 6, and reducing the problem to the existence of a 6-variable Boolean function with nonlinearity greater than 26 whose Hamming distance to a bent function is between 4 and 6.

cs.IT · cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.32192

Identifying codes of Archimedean grids

Rudini Sampaio

原文摘要

An identifying code of a graph is a dominating set $C$ such that the sets $N[v]\cap C$ are pairwise distinct. The minimum density of an identifying code has been studied extensively for several infinite grids. It is known to be $1/4$ for the triangular grid and $7/20$ for the square grid, whereas for the hexagonal grid the problem is still open, between $5/12$ and $53/126$. These three grids are the regular members of the family of the eleven Archimedean grids. In this paper, we start a systematic study of identifying codes in this family. We determine the minimum density for five of the nine Archimedean grids for which it was not known: it is $2/3$ for the truncated hexagonal grid, $3/7$ for the truncated square grid, and $1/3$ for the trihexagonal (kagome) grid, the elongated triangular grid and the snub hexagonal grid. All lower bounds are proved by the discharging method. For the three remaining non-regular Archimedean grids, we also obtain some bounds: the minimum density lies between $3/7$ and $13/30$ for the truncated trihexagonal grid, between $1/3$ and $11/30$ for the rhombitrihexagonal grid, and between $2/7$ and $9/28$ for the snub square grid. As a by-product, we prove that every identifying code of a cubic graph with the slow growth property in which every vertex lies on exactly one triangle (resp. $4$-cycle) has density at least $2/3$ (resp. $3/7$).

math.CO · cs.DM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36168

PrinGS: Privacy Preservation in Gaussian Splatting

Gaurav Behera, Abhinav Raundhal, Archisha Panda, Saumya Balina, Gargi Shroff, Ponnurangam Kumaraguru

原文摘要

3D Gaussian Splatting (3DGS) has rapidly become the preferred representation for high-fidelity novel view synthesis and 3D scene reconstruction. However, the learned representation encodes sensitive visual information including human faces, license plates, documents, and computer screens, raising significant privacy concerns when trained splats are shared or deployed. In this work, we address the underexplored problem of privacy preservation in Gaussian Splatting by proposing a representation-level anonymization framework that directly anonymizes a trained splat. Given a trained splat and a user-provided reference view identifying sensitive content, our method localizes the corresponding regions in the underlying 3D representation and directly anonymizes them, ensuring that the sensitive information is absent from all subsequently rendered novel views while preserving the overall scene geometry and visual fidelity. We achieve this post-training anonymization in less than 1 second. Furthermore, we extend our framework with a privacy-by-design training strategy that prevents sensitive content from being embedded into the representation during optimization, enabling the generation of privacy-preserving splats directly from training images. Our method achieves a remarkably low Face Similarity Measure (FSM) of 9.79% at a retained Face Detection Rate (FDR) of 39.47% and makes sensitive text completely unreadable with an OCR Character Error Rate of 1.00. Our work aims to serve as the primary stepping stone towards privacy-aware 3D representations and motivates further research along these directions.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39385

UMI-Live for In-The-Wild Bimanual Robotics Data Collection

Govind Velamoor, Adrian Lin, Sam Jung, Spencer Mateega, Brandon Y. Yang, Amish Sethi

原文摘要

Universal Manipulation Interface (UMI) devices allow human experts to demonstrate tasks for robot learning without teleoperating physical robots. Using these demonstrations as training data still requires a consistent hardware setup, calibration, and data processing. We present UMI-Live, a full-stack open-source bimanual UMI system for in-the-wild data collection. We design our custom UMI-Live hardware to integrate camera and LiDAR--inertial sensing, onboard compute, and power, without external tracking equipment or calibration within the collection environment. We implement a simple user interface for recording data while an asynchronous pipeline performs parallel SLAM reconstruction and publishes training-ready datasets. We also release software that supports dual-YAM teleoperation and VLA policy training and inference. We validate UMI-Live data quality by fine-tuning $π_{0.5}$ and MolmoAct2, achieving success rates of 10/10 and 8/10, respectively on a bimanual pick-and-place task. Furthermore, we release 40 hours of UMI-Live data across three tasks, and complete hardware designs, software, and reproduction guides at our $\href{https://umi-live.github.io/}{\text{project website}}$.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.24153

Differential Unfolding: Efficient Reconstruction for Video Snapshot Compressive Imaging

Yin-Ping Zhao, Muyuan Zhang, Jiaxi Li, Jiancheng Zhang, Haijin Zeng, Xuelong Li

原文摘要

Although Deep Unfolding Networks (DUNs) are predominant in Video Snapshot Compressive Imaging (SCI), they are still limited by a uniform design philosophy. The existing approaches stack high-complexity priors with the same structure repeatedly, neglecting the fact that the optimization paths gradually converge to a nearly static state. As a consequence, representation stagnation occurs, and much computational cost is wasted on small feature updates. To solve this inefficiency, we propose Differential Unfolding (DU), a heterogeneous framework that substitutes repeated work with a periodic schedule of heavy and lightweight stages. The core of DU is the Differential Evolutionary Framework (DEF), which divides the unfolding procedure into two complementary roles: structural anchoring and differential evolution. In this method, high-parameter general stages are used sparsely to form high-fidelity feature foundations. On the other hand, lightweight differential stages use a Differential Representation Prior (DRP) to pass on and refine these foundational features by a differential mechanism. By integrating Differential Representation Attention (DRA) for evolving attention maps and a Differential Modulated FFN (DM-FFN) for feature rectification, DRP effectively models cross-stage variations with minimal overhead. By concentrating high-capacity computation on the sparsely-scheduled general stages and delegating incremental refinement to the lightweight differential stages, DU achieves a superior trade-off between accuracy and efficiency. Extensive experiments show that DU-9stg achieves state-of-the-art reconstruction quality, while DU-5stg surpasses all compared methods with the fewest parameters and FLOPs; compared with the unfolding method DADUN, DU-9stg needs about half the parameters and a quarter of the FLOPs. Code: https://github.com/Muyuan-Zhang/DU

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39380

Learning to Formulate: Agentic Hardware-Software Co-Design of AI Infrastructure through Progressive Optimization and Verification

Yi Ding, Li Shang

原文摘要

AI infrastructure, the chips and software that train and serve AI models, is designed in layers. Layers make the system manageable, but they also fix which choices an optimizer can consider. Gains that need the chip and its software to change together are excluded before optimization begins: the formulation leaves them out. Language-model agents make it possible to revisit these boundaries, but an agent's proposal cannot show by itself that a design is correct or optimal. We present Formulaic, a flow that divides the work. The agent writes the formulation, a mathematical solver optimizes within it, and verification and measurement check its assumptions and results. The agent refines the problem step by step, from the computation graph toward the chip. Each step is a template: a family of programs with the conditions under which they are correct. Wall relaxation searches the chip by pricing the hardware limits that bind the software. This paper takes a first step, through separate studies rather than one end-to-end run. On NVIDIA GB10, templates the agent wrote run complete Qwen3-8B layers $1.075\times$ faster than SGLang. In a simulated Ventus GPU study, joint design cuts the worst-scenario cycles of Qwen2.5-0.5B to 0.428 of the reference, against 0.934 for software alone. On a five-step GEMM refinement chain, reusing proofs makes checking $2.51\times$ faster.

cs.AR · cs.AI · cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.02959

The Direction of Ignorance: LLMs Encode and Temper a Unigram Prior

Toni J. B. Liu, Jiajun Bao, Gurbir Arora, Mitchell Ostrow, Yizhou Liu, Raphaël Sarfati, Nicolas Boullé, Christopher J. Earls

原文摘要

What does a language model predict when it has few clues?The answer lies in its unembedding geometry: a single direction of the unembedding matrix encodes the unigram distribution of the training corpus, which serves as the Bayesian prior the model falls back on when uncertain. This structure, termed the direction of ignorance, appears across all model families examined (Llama, Qwen, Gemma, and Pythia), ranging from 0.4B to 405B parameters. Projecting the final prediction state onto this direction yields a per-token prior loading factor $λ$, which declines steadily as the context becomes more informative. Formally, the same projection decomposes the prediction state into two orthogonal vectors interpretable as the two components of a tempered Bayesian update: a unigram prior raised to the exponent $λ$ and a context-driven likelihood. This geometric probabilistic connection makes $λ$ meaningfully comparable across model sizes and families. Larger models generally exhibit lower prior reliance, and reasoning models exhibit lower reliance than their base counterparts. Finally, we show that $λ$ serves as a causal, inference-time control: reducing it can promote context-driven continuations and improve Llama-3.2-3B-Instruct on GSM8K, whereas amplifying it or applying matched perturbations along random directions degrades performance.

cs.LG · cs.AI · cs.CL · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39379

Information-Driven Collaborative Autonomy for Heterogeneous UAV/UGV Teams

Joshua Yuriv Pilipovsky

原文摘要

This paper considers coordinating a heterogeneous team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) traversing a domain containing an unknown number of potentially hazardous targets. The UAVs provide aerial sensing using onboard optical cameras, adapting their trajectories to ground routes and target information. We formulate the aerial search problem using ergodic control, which distributes sensing effort according to a spatial information density to balance exploration and target localization. The formulation incorporates dynamic camera field-of-view footprints and pose-dependent probabilistic detection models. Angles-only measurements are processed through Bayesian inference and marginalization over data-association hypotheses to obtain a Poisson multi-Bernoulli (PMB) approximation of the target posterior, representing both detected and undetected targets. This belief directs aerial sensing toward unexplored regions along the ground routes and viewpoints that improve target localization. Ground planning combines chance-constrained target avoidance, enforced through confidence sets, with a void-probability objective that favors routes unlikely to contain threats. We solve each team's optimization problem in a fully decentralized manner using a nested SCvx*--C-ADMM scheme, with outer sequential convex programming iterations and inner distributed consensus optimization, accounting for nonlinearities, nonseparable objectives, and continuous-time inter-agent separation constraints. A closed-loop scheme updates both fleets' trajectories as information becomes available and permits ground motion when the remaining route risk falls below a prescribed threshold. Numerical case studies examine sensing geometry and target uncertainty, compare decentralized and centralized solvers, and demonstrate full closed-loop collaboration between multiple UAVs and UGVs.

cs.RO · eess.SY · math.OC

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新论文 · v1arXiv:2610.39378

Limit Theorems and Optimal Policies for Flock Flight through Poisson Forests

Lav R. Varshney

原文摘要

Inspired by birds flying through cluttered environments, Karaman and Frazzoli showed that high-speed flight through an ergodic random forest exhibits an ultimate speed limit: above a critical speed, almost surely no infinite collision-free trajectory exists for a single bird. This paper formulates a corresponding bulk-transport problem for many birds. We construct an explicit directed corridor network in a Poisson disk forest and impose a common temporal headway at its waypoints. The maximum current equals a minimum vertex-cut capacity and is achieved by repeated flight along vertex-disjoint paths. The same schedule maximizes completed traversals at every finite horizon, with constant forward speed and no internal waiting. For a forest belt of fixed depth, exact transverse superadditivity yields an almost-sure deterministic current-density limit. A uniform shape theorem for an asymmetric planar dual identifies the current when depth and width grow together. For diverging depth bounded by a constant times width, this current equals the width-first, depth-second limit and is independent of the positive aspect ratio. When diagonal corridors are available, bulk current is positive at sufficiently low obstacle intensity and zero at sufficiently high intensity. For stochastic demand, an upstream admission queue is stable at every rate below the finite-network capacity.

eess.SY · cs.RO · math.PR

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新论文 · v1arXiv:2610.39377

AgentDecept: Benchmarking Deceptive Behavior in Tool-Using LLM Agents under Pressure

Yehan Zhang, Qianqi Yan, Hang Yin, Ching-Chen Kuo, Shan Jiang, Ming Ouyang, Yijie Liu, Xin Eric Wang

原文摘要

LLM agents increasingly carry out multi-turn tasks across everyday applications, yet users often see only the agent's report rather than the observations, API calls, and state changes behind it. Existing deception benchmarks have studied long-horizon interactions and tool-use settings, but whether agents faithfully report their actions across realistic, stateful application workflows remains underexplored. To address this gap, we introduce AgentDecept, an executable benchmark for evaluating behavioral deception under pressure. AgentDecept contains 477 pressure variants derived from 257 source tasks across six pressure sources, each paired with a pressure-free counterpart. Interventions enter through event messages, task context, application state, tool responses, and service quotas, spanning explicit events and implicit task or environment changes. Our evidence-grounded protocol evaluates full trajectories by relating user-facing claims to time-ordered observations and executed outcomes, distinguishing falsification, concealment, and equivocation from honest failure and disclosed limitations. The resulting behavioral labels also provide a setting for testing existing activation-based probes. AgentDecept enables controlled analysis of when multi-turn agents' reports diverge from execution in realistic application interactions.

cs.AI

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新论文 · v1arXiv:2610.39374

DualGuard: Comprehensive and Efficient Memory Safety across Sequential and Speculative Execution

Sixuan Dang, Quan Zhou, Kaiming Huang, Jianan Zhu, Anitha Gollamudi, Trent Jaeger, Danfeng Zhang

原文摘要

Although decades of research have yielded sophisticated mechanisms for enforcing memory safety, speculative execution can subvert these protections through transient memory operations. Conversely, defenses designed specifically for speculative safety, such as Speculative Load Hardening (SLH), cannot enforce memory safety during non-speculative execution. A naive combination of defenses from both domains raises two fundamental challenges: (1) which combination of otherwise fragmented defenses can ensure dual memory safety under speculative and non-speculative executions? (2) whether such combinations introduce redundant protections that incur unnecessary performance overhead? We present DualGuard, a unified framework for dual memory protection under non-speculative and speculative executions. DualGuard systematically analyzes memory behaviors, classifying them into four categories, and further identifies the minimal composition requirements for dual memory safety, which allow us to strategically combine existing mitigations and design new ones to avoid redundant or excessive protections. We implement DualGuard through a combination of static analysis and runtime enforcement within the LLVM infrastructure. Our evaluation demonstrates that DualGuard reduces overhead by 3%-72% compared to naive compositions of fragmented defenses. In some cases, it even incurs lower overhead than approaches that guarantee memory safety solely under either non-speculative or speculative execution.

cs.CR · cs.PL

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新论文 · v1arXiv:2610.39373

What Makes Medical Examination Items Difficult? Using Large Language Models to Explore Patterns Associated With Empirical Item Difficulty

Xiaomeng Xiong, Marcus Walker, Yanlin Jiang

原文摘要

Research on medical item difficulty has largely focused on prediction rather than on identifying interpretable patterns associated with empirical difficulty. This study used empirical difficulty labels to guide large language models (LLMs) in inducing difficulty rubrics and examined how rubric guidance affected LLM difficulty judgments. Using training items and their empirical difficulty estimates from the United States Medical Licensing Examination (USMLE) and the Physician Assistant National Certifying Examination (PANCE), Claude Opus 5 induced separate rubrics for absolute and pairwise difficulty judgment. None of the induced rubrics treated surface features, such as stem length, option count, or images, as major sources of difficulty. Several patterns emerged across both examinations, including cue convergence, misleading cues, and option competition, although their specific forms differed. Retrieval of knowledge that could not be derived from the stem was particularly prominent in the PANCE rubrics, whereas the USMLE rubrics placed greater emphasis on integrating multiple clinical cues. We compared commercial and open-weight LLMs on held-out items under unguided and rubric-guided conditions. Rubric guidance changed how models used the difficulty scale but did not consistently improve performance. In absolute judgments, guidance encouraged broader use of the scale and generally improved coarse-grained agreement, without consistently improving rank ordering. In pairwise comparisons, its effects varied across models and examinations, and judgments became more sensitive to presentation order. These findings suggest that the value of label-guided rubric induction may lie more in generating interpretable, testable hypotheses from empirical difficulty data than in reliably improving difficulty judgments.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39372

Cord: Confidence-Anchored Order Residual Decoding for Masked Diffusion Language Models

Chengwei Zhou, Zhaoyan Jia, Massoud Pedram, Gourav Datta

原文摘要

The decoding order of masked diffusion language models (dLLMs) can substantially affect generation quality, yet common decoders typically apply the same reveal rule throughout generation. Confidence provides a strong default, but it measures how decisively a position can be predicted now, not how useful revealing that position will be for subsequent decoding. We propose Cord, a lightweight prompt-conditioned policy that retains confidence decoding as a stable reference and learns when and how to depart from it through geometric residuals over generation direction, proximity to revealed context, and two-sided infilling. The residuals adapt to both the prompt and the evolving partial response. Rather than sampling a new policy action at every decoding step, Cord samples a single low-dimensional coefficient matrix per rollout, which induces an entire state-dependent reveal trajectory. This yields a contextual-bandit formulation trained directly from sequence-level rewards, without step-level credit assignment or backbone updates. Across three dLLMs and five reasoning, code, and constraint-satisfaction benchmarks, Cord improves average task success from 32.0% under confidence decoding to 48.2%, and further improves over the best per-domain fixed residual. Behavioral audits show that the learned residuals induce substantial, task-dependent changes in reveal locality and two-sided infilling. Cord adds only $0.53$M trainable parameters, requires no additional backbone forward passes, and incurs only 5.39% inference latency overhead.

cs.CL · cs.AI

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新论文 · v1arXiv:2610.39371

Lightweight Underwater Acoustic Vessel Classification for Embedded Deployment

Abishek Soti, Thura Pyae Sone, Naqib Ibnul, Jim Huang, Sergio Chevtchenko, Henry Zhong, Gregory Cohen, Ying Xu

原文摘要

We propose a Raspberry Pi-based underwater vessel classification system for edge deployment under limited connectivity, bandwidth and power in offshore acoustic surveillance. Using ShipsEar, we compare six classifiers on a Raspberry Pi 5 through a complete audio-to-prediction pipeline. We assess classification performance, processing latency, CPU utilisation, memory consumption and monitored power. Supporting experiments examine acoustic feature selection, statistical pooling and Transformer patch configurations. The two-layer convolutional neural network (CNN) achieved a mean macro-F1 of \textbf{(99.14 \textpm{} 0.32)\%} across five random seeds. The radial basis function support vector machine (RBF-SVM) achieved \textbf{98.83\%}, with a median end-to-end latency of 28.109~ms compared with 120.655~ms for the CNN. This favourable trade-off motivated RBF-SVM deployment on the Raspberry Pi Zero 2 W. It maintained the same classification performance with a median end-to-end latency of \textbf{349.09~ms} and a sustained throughput of \textbf{2.962 classifications/s}, or \textbf{14.8} times the rate required for consecutive five-second audio segments.

cs.SD

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33005HF 社区推荐

OpenEBFM: Energy-based Foundation Models Are Scalable Architecture

Yuan Pu, Yudong Lu, Ting Wang, Zhicong Hong, Qinuo Li, Xuansheng Wu, Na Zou, Xia Hu 等 9 位作者

原文摘要

Energy-based models have shown promising properties for domain-specific tasks but remain challenging in modeling general-purpose scenarios. To bridge this gap, we design energy-steered models (ESMs) for foundational language modeling. ESMs integrate energy steering and token generation within a single architecture to leverage the energy-guided search to refine token generation. Specifically, ESMs first optimize a latent state over the energy landscape, then leverages the refined latent state for token generation. By performing such energy-guided pre-training on large-scale corpora, ESMs learn generally effective energy landscape where favorable tokens correspond to lower energy values. To explore the scaling behavior of ESMs, we pre-train ESMs on three large-scale corpora, then evaluate across 26 downstream tasks spanning a broad range of domains. Our results demonstrate that ESMs have promising scaling behaviors with increasing model size and training data, and outperform GPTs on most downstream tasks. These findings highlight ESMs as a promising architecture for large-scale pre-training and general purpose problem solving. We fully release the model weights and training infrastructure to enable the community to completely reproduce and extend our results. GitHub: https://github.com/datamllab/openebfm HuggingFace: https://huggingface.co/collections/guan-wang/openebfm

cs.CL · cs.LG

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新论文 · v1arXiv:2610.39367

Utility-WAM: Correcting Future-Induced Action Changes

Feihong Zhang, Xi Lin, Yulong Shi, Zuxing Lu, Yanghong Mei, Zhaopeng Gu, Tingyu Yuan, Zihao Liang 等 10 位作者

原文摘要

World Action Models (WAMs) can condition robot actions on generated visual futures, but this additional context is neither cost-free nor uniformly beneficial. We examine what a predicted future actually changes: the action delta $Δa$ between the future-conditioned and future-free execution paths of the same model. Under a shared WAM checkpoint, this delta barely shifts average benchmark success ($+0.8$ points on LIBERO, $-0.8$ on RoboTwin~2.0), while generating visual futures increases inference latency by 1.7--1.9$\times$. These aggregate metrics conceal a concentrated failure mode that we term \emph{future-induced negative transfer}: while predictive deltas benefit long-horizon tasks ($+2.4$ points on LIBERO-Long) and remain negligible on most routine manipulations, they induce severe degradation on precision tasks---with \emph{Press Stapler} and \emph{Place Can Basket} losing 54.0 and 34.0 points, recurring as top failure modes across independently trained WAM variants. We introduce \textbf{Utility-WAM} for in-place correction of future-conditioned control. Its primary learning component is a residual adapter that repairs future-induced velocity deltas under a frozen shared WAM by aligning them with ground-truth dynamic guidance and penalizing excess error beyond the future-free baseline. The adapter selectively eliminates the majority of severe task drops (reducing losses to 6.0 and 14.0 points) while preserving long-horizon foresight. To eliminate superfluous video computation, a lightweight pre-generation gate reuses counterfactual action outcomes to selectively bypass video generation on 75.5\% of RoboTwin queries, cutting latency by 38\% while improving average success to 93.3\% on RoboTwin~2.0 and 98.9\% on LIBERO. On physical bimanual manipulation across three real-world tasks, Utility-WAM succeeds in 53 of 60 trials, compared to 43 for FAST-WAM.

cs.RO

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版本更新 · v3arXiv:2605.14736

IsoNet: Face-Guided Speech Extraction for Compact Microphone Arrays

Dinanath Padhya, Sajen Maharjan, Binita Adhikari, Ishwor Raj Pokharel

原文摘要

Listening to one chosen talker in a crowded room is effortless for people but hard for small devices, whose microphone arrays are too compact for classical beamforming to separate nearby voices. We present IsoNet, a select-to-listen system: the user selects a face in the camera view, and a 4-microphone array with a 9.4 cm aperture returns that person's speech. IsoNet fuses multi-channel complex spectra, GCC-PHAT spatial cues, and a face embedding at the bottleneck of a U-Net mask estimator, trained with auxiliary direction-of-arrival supervision and an SNR curriculum on simulated reverberant VoxCeleb mixtures. On a hard test set spanning -1 to 10 dB SNR, IsoNet reaches 9.31 dB SI-SDR, a 4.85 dB improvement over the mixture, and raises PESQ from 1.40 to 2.13 and STOI from 0.72 to 0.84. The gains are largest where the task is hardest (5.71 dB SI-SDR improvement at -1 to 1 dB SNR), and IsoNet outperforms oracle-steered delay-and-sum and MVDR beamformers by 9.7 and 10.9 dB SI-SDR. Ablations show that visual and spatial cues are complementary, and a 4.9M-parameter audio-spatial variant stays within 0.16 dB of the full model while running 26 times faster than real time.

cs.SD · cs.LG

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版本更新 · v2arXiv:2509.23122

Stochastic Interpolants via Conditional Dependent Coupling

Chenrui Ma, Xi Xiao, Tianyang Wang, Xiao Wang, Yanning Shen

原文摘要

Existing image generation models face critical challenges regarding the trade-off between computation and fidelity. Specifically, models relying on a pretrained Variational Autoencoder (VAE) suffer from information loss, limited detail, and the inability to support end-to-end training. In contrast, models operating directly in the pixel space incur prohibitive computational cost. Although cascade models can mitigate computational cost, stage-wise separation prevents effective end-to-end optimization, hampers knowledge sharing, and often results in inaccurate distribution learning within each stage. To address these challenges, we introduce a unified multi-stage generative framework formulated under the stochastic interpolant formalism with our Conditional Dependent Coupling strategy as a Flow-Matching variant. It decomposes the generative process into interpolant trajectories at multiple stages, ensuring accurate distribution learning while enabling end-to-end optimization. Importantly, the entire process is modeled as a single unified Diffusion Transformer, eliminating the need for disjoint modules and also enabling knowledge sharing. Under explicitly stated assumptions, we provably reduce both transport cost and asymptotic inference time; we empirically validate the underlying NFE-transport-cost and per-evaluation-cost relations. Extensive experiments demonstrate that our method achieves competitive fidelity and substantially lower wall-clock cost in the low-NFE regime across multiple resolutions.

cs.CV

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