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新论文 · v1arXiv:2611.03871

Survival-Time Risk Beyond Low-Order Channel Models: Identifiability and Scheduling

Ruifeng Zheng, Zhihan Xu, Pit Hofmann, Frank H. P. Fitzek

原文摘要

This paper investigates whether low-order packet-loss statistics suffice to assess survival-time risk under bursty packet losses, i.e., the probability that no update succeeds for a prescribed number of consecutive slots. Conventional models, such as the Gilbert model, characterize packet losses using the loss rate and mean burst length, but channels sharing these statistics may exhibit substantially different failure risks. We characterize the Markov channels one order above a given low-order fit that share its statistics and analyze their risks under acknowledgment-based scheduling with transmission budgets. We show that, with dense transmission attempts, risk is primarily governed by loss persistence beyond the modeled memory, and the worst-case channel need not be the most persistent one. Under limited budgets, retry spacing makes loss relapse probabilities important and can reverse the risk ordering of compatible channels. Numerical and measurement results show that low-order models can substantially underestimate the risk, highlighting the need for scheduling-dependent channel statistics.

cs.NI · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03868

EvoGroup: A Self-Evolving Harness for Group Agents in Shared Contexts

Xinyi Xu, Xiaoyang Li, Yucheng Feng, Wenqing Chen, Youliang Yuan, Wei Shi, Rongxiang Weng, Zibin Zheng

原文摘要

With the rise of Group Agent such as Claude Tag, agents increasingly serve as digital workers for long-term team collaboration. These agents operate over shared contexts that continuously accumulate through multi-user interactions. However, existing agent memory systems are mainly designed for individual users and struggle to capture group-level knowledge in shared contexts, including group relationships and collaboratively formed knowledge. Manual memory strategies further limit adaptation to diverse and evolving shared contexts. To this end, we propose \textbf{EvoGroup (EvoG)}, a self-evolving harness for group agents that starts from a minimal six-tool cold start and adapts through interaction with shared context. EvoG uses confidence and task outcomes to select informative trajectories, while also eliciting self-reflections on uncertainty in low-confidence cases. The analysis agent then examines selected trajectories through adaptive progressive disclosure, followed by bucketed analysis to synthesize high-level findings that guide the evolve agent in revising the Harness. Empirically, pass@1 on held-out split of EverMemBench improves from 71.07\% to 89.58\%, surpassing both self-evolving memory and human-designed harnesses, including Claude Code (85.83\%). To evaluate generalization, we freeze the evolved harness and evaluate it on the unseen GroupMemBench, where, with GPT-5.5, it demonstrates effective generalization and outperforms both memory baselines and human-designed harnesses.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03867

ExaTune: Exhaustive Hyperparameter Landscape Exploration at Scale

Kevin Dick, Katie Lonsway

原文摘要

Hyperparameter optimization (HPO) returns a single optimal configuration, systematically discarding the explored performance landscape that encodes how results depend on configuration choices. Fitness landscape analysis comprises a robust collection of methods from evolutionary computation offering ruggedness indices, autocorrelation functions, and fitness-distance correlation that have never been applied to machine learning hyperparameter spaces at scale, given that fine-grained ground-truth enumeration data has not existed. We present ExaTune, an open-source HPC system to produce a new artifact class: the hyperparameter landscape map. ExaTune exhaustively evaluates every point on a discretized hyperparameter grid via $K$-fold cross-validation, aggregates results into reproducible parquet benchmarks, and applies a suite of five formalized landscape metrics: spatial autocorrelation (Moran's $I$), ruggedness, local optima density, fitness-distance correlation (FDC), and variance-based parameter importance. We release a benchmark collection of 8 complete landscape maps (totalling 285,080 exhaustively evaluated configurations) spanning five model families (SVMs, random forests, XGBoost, ridge regression, and $k$-nearest neighbors) across six classification and two regression benchmarks, hosted on HuggingFace. Empirical analysis demonstrates that Bayesian optimization, Optuna, Hyperband, and Random Search at standard trial budgets systematically miss instability regions, secondary optima, and parameter interaction effects that are visible in complete landscapes; information that is structurally unavailable to any optimizer by design.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.00812

Univalence without function extensionality

Evan Cavallo, Jonas Höfer

原文摘要

It is a well-known theorem of homotopy type theory, originally due to Voevodsky, that function extensionality holds inside any univalent universe. We consider a weaker variant of the univalence axiom, asserting that the wild category formed by the universe is univalent, which we call categorical univalence. We show that categorical univalence does not imply function extensionality by an analysis of Von Glehn's polynomial model construction, which produces models of Martin-Löf type theory that always refute function extensionality. We find in particular that when the base model has a univalent universe, its polynomial model has a universe that is categorically univalent but lacks function extensionality.

cs.LO · math.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.02457

MetaCLASS: Metacognitive Coaching for Learning with Adaptive Self-regulation Support

Naiming Liu, Richard Baraniuk, Shashank Sonkar

原文摘要

Large language models can generate fluent explanations, but effective tutoring requires supporting the learner's thought process, not just delivering content. Metacognitive tutoring targets this gap by prompting planning, monitoring, debugging, and evaluation, and, crucially, deciding when to be active versus minimally present, based on learner signals and trajectory. We introduce MetaCLASS, a learning-science grounded framework that formulates metacognitive tutoring as move selection over 11 interpretable actions aligned to self-regulated learning (SRL) processes. MetaCLASS uses a two-phase procedure that first plans a pedagogical trajectory conditioned on learner profiles (calibration, help-seeking) and then generates natural dialogue consistent with that plan. This process yields 1,015 conversations spanning 7,711 turns, each annotated with turn-level metacognitive labels and supported by comprehensive validation analyses. We benchmark nine open-source LLMs on predicting the next coach move given a question and dialogue context. The best model achieves only 43.2% accuracy in coach move prediction, and models exhibit compulsive intervention bias: in turns where effective metacognitive tutoring requires silence (41.7% of cases), models predict "no intervention'' only 6.6% of the time, while severely over-predicting high-intervention moves. These results show that traditional content-based tutoring ability does not translate to metacognitive tutoring competence, positioning MetaCLASS as a testbed for developing tutors that promote SRL.

cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03862

Dr. No: Feature Steering for Abstention in Vision-Language Models

Farid Hasanov, Riccardo Renzulli, Bahram Zonooz, Mykola Pechenizkiy, Decebal Constantin Mocanu

原文摘要

Large foundation models often answer even when the available visual or textual evidence is insufficient to support a reliable conclusion. This failure to abstain makes them hard to trust in high-stakes or information-seeking settings, where a confident but unsupported answer can be worse than no answer at all. It also raises a natural question: can the internal mechanisms behind this behaviour be identified and controlled at inference time? We present Dr. No, a feature-steering approach that mines sparse autoencoder (SAE) features associated with abstention and commitment, then modulates a small set of them during the prefill pass, without retraining the base model. Dr. No is evaluated on LLaVA-1.6 Mistral 7B across multimodal abstention benchmarks, including UNK-VQA and MM-UPD, and additionally benchmarked on language reasoning tasks using Qwen3.5-2B on AbstentionBench subsets. Against multiple uncertainty-based abstention baselines, feature steering achieves a more favourable balance between correct answering and appropriate abstention than post-hoc rejection methods. The gains are strongest on UNK-VQA and on MM-UPD cases involving image--question or question--answer incompatibility, and transfer most clearly to mathematical reasoning subsets of AbstentionBench. However, performance is more limited on absent-answer detection and retrieval-heavy multi-hop QA, such as MuSiQue. These results suggest that sparse internal features provide lightweight control handles for shaping uncertainty-aware behaviour in foundation models, while also showing that abstention control is source-dependent rather than governed by a single universal signal.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03861

Predictive gains, causal losses: why interventional competence cannot be learned from data alone

Marcus Thomas

原文摘要

Retaining predictive gains can require persistent errors in a model's response to interventions. For deterministic predictors with finite second moments under squared-error loss, any observational advantage over the best predictor using only an outcome's direct causes requires dependence on other inputs, regardless of the learning rule. When admissible interventions vary inputs while preserving the worldly outcome, this dependence yields a positive lower bound on root-mean-square intervention-response error. We identify conditions under which population gradient updates introduce this dependence from a parent-only baseline or damage a previously correct mechanism through shared parameters while improving other predictions. These dependencies can also make model behavior vary with how the same intervention is realized. We characterize interventional alignment through a common output mapping across internal states and physical processes realizing the same intervention target and relevant context. Correction is limited by the causal hypotheses currently admitted within inquiry. The relevant hypothesis class comprises these causal alternatives, rather than the function class the computational system could in principle represent. For predictions restricted to these alternatives, interventional mean error cannot fall below the best approximation they permit, whether evidence is observational or interventional. Reducing error below a positive limit requires admitting better hypotheses, which may entail revising the causal variables, mechanism structure, or intervention interpretation. Interventional criticism constrains replacements without determining them. We conjecture that reliably acquiring and maintaining interventional competence when causal commitments fail requires autonomous participation in proposing alternatives, testing their consequences, and revising them in response to error.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.00312

Quantifier Elimination Meets Treewidth

Hao Wu, Jiyu Zhu, Amir Kafshdar Goharshady, Jie An, Bican Xia, Naijun Zhan

原文摘要

In this paper, we address the complexity barrier inherent in Fourier-Motzkin elimination (FME) and cylindrical algebraic decomposition (CAD) when eliminating a block of (existential) quantifiers. To mitigate this, we propose exploiting structural sparsity in the variable dependency graph of quantified formulas. Utilizing tools from parameterized algorithms, we investigate the role of treewidth, a parameter that measures the graph's tree-likeness, in the process of quantifier elimination. A novel dynamic programming framework, structured over a tree decomposition of the dependency graph, is developed for applying FME and CAD, and is also extensible to general quantifier elimination procedures. Crucially, we prove that when the treewidth is a constant, the framework achieves a significant exponential complexity improvement for both FME and CAD, reducing the worst-case complexity bound from doubly exponential to single exponential. Preliminary experiments on sparse linear real arithmetic (LRA) and nonlinear real arithmetic (NRA) benchmarks confirm that our algorithm outperforms the existing popular heuristic-based approaches on instances exhibiting low treewidth.

cs.LO · cs.CC · cs.SC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03858

A Family of Well-Behaved Multiflow Instances

Joseph Poremba, F. Bruce Shepherd

原文摘要

We introduce a family of cut-sufficient instances for undirected multiflow that generalizes single-source planar and fully-planar instances. We also show the family has an additive $6 d_{\max}$ error for converting a fractional flow into an unsplittable one.

cs.DS · cs.DM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.10942

StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents

Daksh Raghuvanshi, Ved Vedere, Yifan Wang

原文摘要

Reinforcement learning environments are now a primary lever for improving large language model (LLM) capabilities in post-training, yet most agentic benchmarks remain static: the world moves only when the agent acts, the reward is a terminal verdict, and the pass bar is set arbitrarily. We introduce StoreBench, a live-commerce environment in which an agent runs a mid-size online apparel store on a production-grade commerce backend, testing long-horizon planning and economic judgment under uncertainty. Customers order around the clock, suppliers reprice and fail, and market shocks arrive with partial or no warning. The agent acts through the same 29 merchant tools a human operator would use, under a windowed operation budget that makes simulated time a function of actions taken, so model latency cannot influence simulated time. Pass thresholds are calibrated against scripted anchor policies, the reward is hardened against a catalogue of reward hacks, and every episode replays identically given a sequence of actions. We evaluate seven frontier LLMs on 11 scenarios of 30 to 45 days and a full simulated year, over three world seeds at matched reasoning effort. No model matches the scripted smart-triage policy on average: the best, DeepSeek-V4-Pro, passes 49% of task-seed cells against the heuristic's 97%. Human experts working through the same tools and budgets outscore every model (mean composite 0.708 vs. 0.700). Over a full simulated year under the Claude Code harness, most models show dramatic performance improvement. In a GRPO post-training run, Qwen3.5-27B trained on only five disjoint tasks raises its mean composite on the held-out evaluation tasks from 0.136 to 0.373. We release five example training-split tasks, ten sample trajectories, and the scoring and verification tooling; the full environment and evaluation suite are withheld to keep the benchmark uncontaminated.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.15999

Trajectory and Morphology Planning for Morphing Quadrotors in Ultra-Narrow Passages using Limited Perception

Harsh Modi, Xiao Liang, Minghui Zheng

原文摘要

This paper presents an online trajectory and morphology planning framework for morphing quadrotors to traverse passages narrower than their unfolded width using only a 2D LiDAR for obstacle sensing. Fixed-frame vehicles cannot pass through such gaps, and classical artificial potential field (APF) based obstacle costs seal them off for planners as well, because the repulsive cost grows sharply inside a tight gap. The core enabler of our framework is a novel cost reduction factor, computed in closed form from the LiDAR data, that cancels the obstacle cost along the medial axis of a narrow passage. A true opening thus becomes a low-cost corridor, while the full repulsive cost is retained everywhere else. The cost reduction factor is embedded at every prediction node of an acados-based nonlinear MPC, which jointly plans the trajectory, yaw, and fold angle of the platform. A user-specified minimum-width limit ensures that the cost reduction factor only opens the corridors that the platform can actually pass. Gazebo/PX4 simulations and hardware experiments on the MorphoCopter demonstrate autonomous folding and unfolding through curved, inclined, and cluttered passages narrower than the unfolded width, where the standard repulsive cost fails to pass safely in most of the trials. The implementation code is available at \href{https://github.com/harshjmodi1996/morphocopter_mpc}{\textcolor{blue}{Github}} and a short video about the paper is available at \href{https://zh.engr.tamu.edu/wp-content/uploads/sites/310/2026/10/Morphocopter_planning_final.mp4}{\textcolor{blue}{\textbf{video link}}}.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.01216

Eco-MobiLLM: Efficient LLM Fine-Tuning on the Mobile Device via Server Assisted Side-Tuning

Xingke Yang, Liang Li, Zhiyi Wan, Sicong Li, Xiaoqi Qi, Jiang Liu, Tomoaki Ohtsuki, Xin Fu 等 9 位作者

原文摘要

There is a huge gap between numerous intriguing applications fostered by on-device large language model (LLM) fine-tuning (FT) from fresh mobile data and the limited resources of a mobile device. While existing server-assisted methods (e.g., split learning or side-tuning) may enable LLM FT on the local mobile device, they suffer from heavy communication burdens of activation transmissions. In addition, their multiple on-device forward passes over the same samples across training epochs lead to substantial iterative computation on the mobile device. To address those issues, we develop Eco-MobiLLM, an efficient LLM FT method which can be deployed on the mobile device via server-assisted side-tuning. To reduce communication cost, Eco-MobiLLM develops an activation shortcut that transmits only the tokens involved in the loss calculation instead of full activation matrices to guide the side network tuning. Besides, to improve computing efficiency and further accelerate FT convergence, Eco-MobiLLM introduces a novel two-stage training paradigm, which allows the server to reuse historical activations and saves the mobile device from repeatedly computing forward passes for the recurring data samples. Extensive experimental results demonstrate Eco-MobiLLM's superiority.

cs.LG · cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.04125

Concentration from Product Moments via an Additional Element of Randomness

Michael Saks, Aravind Srinivasan, Renata Valieva

原文摘要

The standard method of exponential moments for proving concentration bounds can often be replaced by an argument based on elementary symmetric polynomials. We introduce an additional element of randomness into this framework, which reduces the problem to bounding product moments over a uniformly sampled set of indices. We show that this approach gives useful bounds in three settings. For read-$Δ$ families under limited independence, we obtain bounds governed by the degrees of randomly induced dependency subgraphs, improving the dependence on worst-case degrees. For random linear hashing over arbitrary finite fields with (semi-)random inputs, we derive fixed-bin and maximum-load bounds by controlling the rank defect of random tuples of input keys. Finally, for stochastic processes, we show how decay of product moments yields concentration bounds, recovering the spectral and mixing-time scales for finite-state Markov chains.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.14898

Mechanising Denotational Semantics in Agda

Peter D. Mosses, Jesper Cockx, Bernhard Reus

原文摘要

Mechanisation of a mathematical definition (also referred to as formalisation) has many benefits. Here, we focus on mechanising denotational semantic definitions of programming languages by embedding them in the Agda language. The Agda type-checker detects and reports any issues with the wellformedness and type correctness of the embedded definitions. To minimise the effort required, and to facilitate correlation of the original definition with its Agda embedding, mechanisation should not involve significant reformulation or extension. Here, we show how to embed conventional Scott-Strachey denotational definitions in Agda with almost no changes to $λ$-notation or domain equations. Agda notation for definitions of types and functions corresponds closely to the conventional meta-notation of denotational semantics. We have developed a collection of Agda modules with postulated types for commonly used domain constructors and their associated operations. We illustrate our approach with mechanisations of three denotational definitions: a model of the untyped $λ$-calculus, Plotkin's denotational semantics of PCF, and a semantics of a sublanguage of Scheme. In previous work, similar mechanisations in Agda have revealed several unsuspected wellformedness issues in published denotational definitions. Some of the postulated properties of our definitions are inconsistent with a classical set-theoretic interpretation of Agda. We conjecture that they would be consistent with an interpretation of Agda in a higher-order intuititionistic logic, such as that used by Simpson in his work on synthetic domain theory. After recalling previous work on mechanising domain theory, we consider the possibility of mechanising synthetic domain theory in Agda.

cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03854

Towards Effective Fuzzing of EDK2 Components

Datta Manikanta Sri Hari Danduri, Connor Everett Glosner, Aravind Machiry

原文摘要

Securing bootloaders is critical to establishing the trusted computing base of modern systems. TianoCore's edk2, the de facto UEFI-based bootloader, is widely deployed but has recently been shown to contain numerous high-severity vulnerabilities. Existing approaches either focus on post-boot interfaces or rely on heavyweight simulation, limiting their scalability, throughput, and coverage of boot-time components. We present Metis, an automated technique for fuzzing edk2 protocols (i.e., boot-time interfaces) by rehosting them as user-space applications. Our key insight is that effective fuzzing must exercise not only protocol interfaces but also their dependency interactions, which serve as additional input channels. Metis employs a Direct-dependency Component Rehosting (DCR)-based strategy to construct semantically meaningful mocks for dependencies, enabling controlled input injection across all channels. It further leverages a hybrid approach combining static analysis and Large Language Model (LLM)-guided synthesis to generate and refine fuzzing harnesses. We implement Metis on the Host Based Firmware Analyzer (HBFA) framework and evaluate it on 60 drivers covering 162 protocols. Metis achieves 73.1% coverage, approximately 1.7 times higher than FuzzUEr, the state-of-the-art edk2 fuzzing technique, and discovers 137 previously unknown bugs. We have already reported 26 of them, including 12 that are remotely triggerable, and all reported vulnerabilities have been acknowledged by the vendors.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03853

Performance, Scalability and Maintenance: An Experimental Study of RPA vs. Autonomous Agents in the Healthcare Domain

Daniel Schulz, Pedro Cabral Buchaim, Bernardo Soares, Fernando Rafael Ensslin Schwengber, Lorenzo Farias, Daniela Duarte da Silva Bagatini, Ana Paula Wernz da Cunha Müller

原文摘要

Healthcare information systems are fragmented, and retrieving data from legacy web portals is a recurrent need. Robotic Process Automation (RPA) is deterministic but sensitive to interface changes, whereas autonomous agents based on large language models (Agent Process Automation, APA) promise adaptability at a higher computational cost. Following Design Science Research, we developed and compared an RPA solution (Playwright) and an APA solution (browser-use, LangGraph and Gemini 2.5 Flash-lite) to retrieve mammography and breast ultrasound reports from a laboratory portal, treating reports solely as informational artifacts. Across two rounds (T and T+7), RPA reached 98.90% accuracy and 98.27% F1-score versus 96.49% and 94.48% for APA, was about 13 times faster (16.07 s vs. 207.21 s per patient) and cost about six times less per report (USD 0.030 vs. 0.187). Both reached 100% step success rates; APA varied more over time. For a stable interface, RPA was superior.

cs.AI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03852

Why Pay for Long Reasoning When Rewrite Do Trick? Causal Effects of Reasoning History Compaction on Future Generation and Agent Cost

Anton Hristov

原文摘要

Reasoning substantially improves language models' ability to solve difficult problems, but long reasoning traces make these gains expensive. Efficient reasoning should express useful analysis in fewer tokens by reducing repetition and text that adds little value to solving the problem. Progress in proprietary models motivates this goal, yet their hidden scratchpads limit direct study of how it is achieved. We investigate whether open-weight models can generate less verbose reasoning by changing the form of their own earlier thoughts, without retraining. Our intervention rewrites prior reasoning into compact state records before pending tool results are revealed, then compares continuations from original and compact histories. Across the tested models, accepted compact histories usually shorten future reasoning; a coding case study also shows less repeated verification after a shared candidate solution. In software-agent deployments, compaction can reduce total spending, although task quality and rewrite-inclusive time depend on the target and compressor. These findings identify reasoning history as one way to reduce future reasoning-token use and motivate model-specific compaction policies evaluated for both efficiency and retained capability.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2605.06840

Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning

Sixing Chen, Ji-An Li, Saner Cakir, Sinan Akcali, Kayla Lee, Marcelo G. Mattar

原文摘要

Large language models (LLMs), especially reasoning models, generate extended chain-of-thought (CoT) reasoning that often contains explicit deliberation over possible futures. Yet it remains unclear whether this deliberation constitutes genuine planning, how it is structured, and what aspects of it drive performance. Here we introduce a method for characterizing LLM planning by extracting search trees from reasoning traces in the board game four-in-a-row. By fitting computational models to the extracted search trees, we quantify how plans are structured and how they influence move decisions. We find that LLMs search less deeply than humans, and that their performance is better predicted by search breadth than depth. Most strikingly, although LLMs expand deep nodes in their traces, their move choices are best explained by a myopic model that ignores those nodes entirely. A causal intervention that selectively prunes CoT paragraphs supports that move selection depends predominantly on shallow rather than deep nodes. These patterns contrast with human planning, where performance is driven primarily by deep search. Together, our findings reveal a key difference between LLM and human planning: human expertise rests on deeper search, whereas LLMs generate deep lookahead but fail to act on it. This dissociation offers concrete targets for aligning LLM and human planning. More broadly, our framework provides a generalizable approach for interpreting the structure of LLM planning across strategic domains.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03850

RoboScope Kit: Unified Interfaces for Fast and Scalable Embodied-AI Evaluation

Deng-Ke Wu, Yifan Jin, Yida Yang, Lei Qian, Zhaoye Zhou, Hongru Zhang, Kai Ye, Shuyuan Hu 等 11 位作者

原文摘要

Simulation benchmarks are the standard way to compare robot policies, but each benchmark brings its own evaluator, observation layout, and action convention, and evaluation is slow: 500 RoboTwin Easy episodes with one simulator and one model on one GPU take 359.7 minutes. Profiling the official StarVLA evaluator shows that simulation occupies CPU cores while the GPU averages 14.3% utilization. RoboScope Kit wraps each model once in a Python class called a Policy, and each benchmark once in an adapter around the benchmark's own evaluator, so the benchmark's rules and scoring stay unchanged. Scheduling runs simulator workers and Policy replicas as separate pools, sizes both from measured probes, and shards work deterministically across GPU instances; runtime optimization renders only the observations an action-chunk Policy reads. Adapters cover six benchmark families. On LIBERO-Long with a StarVLA checkpoint on one 48 GiB RTX 4090, the concurrency search selects twelve simulator workers sharing three Policy replicas, which evaluate 500 episodes 4.45$\times$ faster than the official StarVLA evaluation with equivalent success rates, 2.78$\times$ faster than RLinf, and 1.52$\times$ faster than vla-eval at its tuned configuration. With CUDA MPS, which lets the replicas run their kernels concurrently, the search selects sixteen workers sharing four replicas, 6.16$\times$ faster than StarVLA, 3.85$\times$ faster than RLinf, and 2.10$\times$ faster than vla-eval. On RoboTwin Easy, four GPUs with chunk-boundary observation complete the 500 episodes in 14.4 minutes, 25.04$\times$ faster than the single-GPU baseline, with identical success outcomes and terminal steps.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.10641

A Deadline-Driven Algorithm for Polyamorous Scheduling

Arjun Maneesh Agarwal

原文摘要

In Polyamorous Scheduling Problem, we are given an edge-weighted graph and must find a periodic schedule of matchings in this graph which minimizes the maximal weighted waiting time between consecutive occurrences of the same edge. This NP-hard problem generalises Bamboo Garden Trimming and is motivated by the need to find schedules of pairwise meetings in a complex social group. We present a $4 G^*$ algorithm for Polyamorous Scheduling, improving the previously known bound of $3 + \sqrt{5} \approx 5.236$. Our algorithm is inspired by the Deadline-Driven Heuristic which is optimal for Bamboo Garden Trimming (BGT).

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03849

LaplaceDiag: Fault Diagnosis through Changes in Learned Actuator Effects

Oliver Niggemann

原文摘要

Identifying faulty components from sensor anomalies is difficult because fault effects can propagate through a plant and different faults can produce similar observations. Detailed component models and labeled fault examples may also be unavailable. We introduce LaplaceDiag, a data-driven method that uses changes in actuator effects to narrow the search for faulty equipment. Actuators act on specific devices and mechanisms, so their changed effects can provide clues to root causes. LaplaceDiag learns a healthy plant model with LaplaceNet, a neural network that operates in Laplace space. It separates nonlinear prediction based on measured history from an explicit matrix that describes how actuator values affect sensor values. Keeping the history predictor fixed allows current actuator effects to be estimated by linear regression without retraining the neural network. Comparison with a healthy reference accounts for estimation uncertainty and normal variation to detect changes and generate actuator-related diagnostic hypotheses. The method supports online and offline diagnosis, providing an intermediate step between anomaly detection and identifying faulty components. We successfully evaluate LaplaceDiag on two datasets.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.26029

Statistical and Computational Limits for Cumulant Tensor Inference: A Regime in Which Efficient Detection Is Harder Than Efficient Estimation

Runshi Tang, Yuefeng Han, Anru R. Zhang

原文摘要

We study estimation and detection of high-order cumulant tensors from $n$ i.i.d. observations of a $p$-dimensional random vector, with performance measured in the tensor spectral norm. Under sub-Gaussianity, we show that the minimax rate for estimating the order-$d$ cumulant tensor is $\sqrt{p/n}\wedge 1$. In contrast to covariance estimation, the natural plug-in estimator obtained by replacing population moments in the moment--cumulant formula with sample moments, which we call the sample cumulant tensor, is generally not rate-optimal for $d\ge 3$, and we construct an estimator that attains the minimax rate. On the computational side, we study testing whether the $d$-th order cumulant tensor vanishes after whitening. We provide evidence, under a model-specific low-degree conjecture, that detection is computationally hard when $n\ll p^{d/2}$. At the same time, we identify a regime in which an efficiently computable estimator achieves error smaller than the separation at which polynomial-time tests can reliably distinguish the null from the alternative under the same conjecture. This reveals an unusual reverse detection--estimation gap: computationally efficient detection can be harder than computationally efficient estimation. The underlying reason is that the relevant loss, tensor spectral norm, is itself NP-hard to compute, creating a new form of computational--statistical gap.

math.ST · cs.CC

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版本更新 · v4arXiv:2605.17112

A unification of graded and substructural logics

Peter Hanukaev, Harley Eades

原文摘要

Type systems which account for resource sensitive computations can generally be split into two styles: First, substructural logics such as Linear Logic which seek to restrict weakening and contraction and reintroduce them in a controlled manner; And second, graded systems which allow weakening and contraction by default, but track the use of variables quantitatively in some algebraic structure -- usually a semiring. We present GRASS (Graded and substructural), a type system which incorporates mechanisms from both of these approaches, thus allowing maximally flexible control over variable usage. Furthermore, GRASS allows grades from an arbitrary collection of grade algebras to coexist in the same system, thus allowing different variables to be controlled with respect to different notions of resource within the same program. We develop the categorical semantics of GRASS, and find that, on the level of categorical semantics, it subsumes multiple established systems such as LNL, Adjoint Logic, and mGL.

cs.LO

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新论文 · v1arXiv:2611.03844

Probabilistic Steering of Generative Priors for Lab-in-the-Loop Antibody Optimization

Anisha Parsan, Sebastian Ober, Calvin McCarter, Nick Bhattacharya, Hunter Elliott

原文摘要

Therapeutic antibody development is often framed as comprising two discrete stages: initial discovery of novel binders followed by iterative optimization to refine these hits into therapeutic-grade molecules. Models and methods often reflect these stages, with generative structure models applied to discovery and probabilistic black-box optimization methods applied to refinement. Here we propose to unify these stages by smoothly transitioning a generative discovery model into iterative optimization, allowing the handoff between the structure-based prior and supervision from wet lab data to be guided by the posterior uncertainty of a probabilistic surrogate. We show that this yields improvements in optimization efficiency in the low-N regime, as well as improved diversity amongst the optimized molecules.

cs.LG

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版本更新 · v2arXiv:2610.03550

Counterexamples to the Strong Roberson Conjecture

Arnar Á. Kristjánsson

原文摘要

We refute the Strong Roberson Conjecture, which asserts that adding any graph outside a graph class closed under minors and disjoint unions strictly increases the distinguishing power of homomorphism counts from that class. More precisely, we construct connected graphs $H$ for which homomorphism counts from graphs excluding $H$ as a minor determine the number of homomorphisms from $H$ to any target graph. We also refute the analogous conjecture with immersions in place of minors. The proof introduces a method for deriving exact homomorphism count dependence from modular equivalences. We obtain these modular equivalences for infinitely many primes using prime-order automorphisms and the corresponding orbit quotients. We complete the refutations by proving that suitable circulant graphs with two jump lengths and related cubic bipartite graphs exclude their corresponding orbit quotients as minors and immersions, respectively.

math.CO · cs.DM · cs.LO

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新论文 · v1arXiv:2611.03839

PianoBench: Evaluating Musical Perception and Reasoning in Language Models

Louis Bradshaw, Irmak Bukey, Daniel Chenyu Lin, John Thickstun, Chris Donahue

原文摘要

Multimodal language models continue to improve on broad music understanding benchmarks, but which musical features can they perceive and reason about? We investigate this question by introducing PianoBench, a benchmark that tasks language models with reasoning about musical performances across congruent acoustic and symbolic input representations. PianoBench is derived from our new multimodal, training-scale dataset comprising more than 20,000 hours of interleaved transcripts of music and spoken musical commentary from instructional piano videos. Open-weight audio-language models achieve at most 52.8% accuracy on PianoBench, and at best only modestly outperform their accuracy on a no-audio control. In contrast, frontier models using chain of thought achieve up to 93.2% accuracy when provided transcripts of music as text, exposing a gap between perceiving musical features from audio and reasoning about musical events when they are given explicitly. We then test whether mid-training on interleaved musical instruction can improve question answering without chain of thought, raising Qwen 3.5 9B Base's zero-shot accuracy on PianoBench with music transcripts from 42.8% to 56.0% after instruction tuning. These experiments suggest that instructional videos may represent an overlooked source of multimodal supervision for music. Available: https://gclef-cmu.org/pianobench.

cs.SD · cs.AI · cs.LG

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新论文 · v1arXiv:2611.03838

A King for Almost Everyone

Ziad Ismaili Alaoui, Oded Lachish, Nikhil S. Mande

原文摘要

A tournament is an orientation of a complete graph. A vertex $v$ controls a vertex $u\neq v$ if there is a directed path of length at most two from $v$ to $u$, and a $δ$-king is one that controls at least $δ(n-1)$ vertices. In the query complexity model, each query reveals the orientation of an edge. Shen, Sheng and Wu [SICOMP'03] established an $Ω(n^{4/3})$ lower bound on the deterministic query complexity of finding a $1$-king, and a non-matching upper bound of $O(n^{3/2})$. Neither bound has been improved in over two decades, despite substantial progress on variants of the problem. For $δ$-kings with $δ< 1$, Biswas, Jayapaul, Raman and Satti [Discrete Applied Mathematics'22] showed that $O(n^{4/3})$ queries suffice to find a $\frac12$-king. Lachish, Reidl and Trehan [FSTTCS'22] improved this guarantee to $\frac12+\frac{2}{17}$ using $\widetilde{O}(n^{4/3})$ queries, and asked whether a larger fraction could be achieved within the same query bound. We answer this question affirmatively for every fixed fraction below one: for every fixed $\varepsilon\in(0,1)$, we give a deterministic algorithm that finds a $(1-\varepsilon)$-king using $O(n^{4/3})$ queries. The design of our algorithm is informed by a structural analysis combining a sparse regularity lemma due to Allen, Kohayakawa, Mota and Parente [Electronic Journal of Combinatorics'14] with a new sampling argument based on a reachability theorem of Afek, Gafni and Linial [arXiv'19].

cs.DS · cs.CC · cs.DM · math.CO

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.11749

Truth as a Compression Artifact in Language Model Training

Konstantin Krestnikov

原文摘要

When a transformer is trained from scratch on corpora that solve the same mathematical problem both correctly and incorrectly, which solution does it prefer? The usual test is pairwise, and it is not neutral: when the error can take several forms, a model that merely reproduces training frequencies wins it without preferring anything. We instead score the correct completion against every string the error generator can produce, a share on which frequency matching scores exactly 0.500. At 86M parameters the correct completion holds 59.0% +/- 0.8% of the mass against random errors (59.4% on an independent corpus), where the single realized error gives 77.6% and pair accuracy 87.4%, a three- to fourfold overstatement of the excess above chance; below 86M the primary corpus shows no preference. The coherent false rules of the main corpora are not filtered at any size (43.5--48.8%; the shortfall below 0.5 is a completion-length effect). Two experiments built to identify a compression mechanism raise the pairwise measures while the summed share stays flat or falls: both are negative controls, and compression remains motivation, not a measured mechanism. With the scored false strings held fixed, a false rule whose selector the model can learn moves the correct share from 5 points above the null to 7 below (418M--3.08B), though the correct completion still outweighs the selected string. What these models leave unfiltered is consistent or learnable false structure (one single-rule corruption excepted), not falsehood as such.

cs.CL · cs.AI

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新论文 · v1arXiv:2611.03836

A Conditional Mutual Information Analysis of Response Variety in Language Models

R. I. Cukier

原文摘要

Large language models provide a variety of responses at inference (generation) that conventionally depend on a temperature parameter. Using a conditional mutual information approach, a quantitative, entropy based analysis of response variety is given. Three limiting behaviors are defined: arbiter, independent (conditionally) and neutral (deterministic) that span no to maximal mutual information. We give examples of each behavior and suggest their application to: temperature analysis, language analysis, prompt compression, and knowledge graphs.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.14955

Convex algebras on an interval with semicontinuous monotone operations

Ana Sokolova, Harald Woracek

原文摘要

In a recent work of Matteo Mio on compact quantitative equational theories (here compact means that all its consequences are derivable by means of finite proofs) convex algebras on the carrier set [0,1] whose operations are monotone and satisfy certain semicontinuity properties occurred. We fully classify those algebraic structures by giving an explicit construction of all possible convex operations on [0,1] possessing the mentioned properties. Our result thus describes exactly the range of theories to which Mio's theorem applies.

cs.LO · math.LO

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