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新论文 · v1arXiv:2611.16304

StateAlign: Internalizing State-Aware Decision-Making for Reliable Multi-Turn Tool Agents via Reinforcement Learning

Zhexi Lu, Subhajit Chaudhury, Tejaswini Pedapati, Keerthiram Murugesan, Lei Yu

原文摘要

Reinforcement learning has improved the tool-use capabilities of large language models. However, better task performance can coincide with increased tool hallucination. Reliable tool use requires decisions to follow the current task state: available information, tool capabilities, and task progress. Our analysis shows that agents sometimes execute when they should ask for information, retrieve it, or decline an infeasible request, even when reasoning identifies missing information. We introduce StateAlign, combining task-state construction with state-contrastive reinforcement learning. The data cover five task conditions and pair states with different action requirements. Outcome rewards guide task completion; token-level contrasts compare the same generated reasoning and actions under paired states to train outputs that fit the current state. Experiments across five base models from three families show improvements on BFCL v4 multi-turn, ToolSandbox, and When2Call. Ablations show that state construction improves performance and reliability under different reward designs. The complete method further reduces tool hallucination across three Qwen3 sizes. On Qwen3-8B, StateAlign improves BFCL v4 multi-turn accuracy from 45.5% to 55.2% and reduces no-tool hallucination on SimpleToolHalluBench from 62.2% under outcome-only training to 56.1%. Behavioral analyses show more consistent decisions, more complete multi-call execution, and stronger recovery after tool errors.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.24969

Rare Event Estimation via Iterative Unalignment

Hanming Yang, Daksh Mittal, Jing Dong, Hongseok Namkoong

原文摘要

As agents are deployed with increased autonomy, even extremely rare events along their stochastic output trajectories can occur and prove catastrophic. Safe deployment therefore does not depend on whether these events can occur, but on how often they might. We study the problem of estimating the probability of rare events that arise from stochastic variation in the agent's own actions. Estimating this type of risk requires searching over the combinatorially vast space of trajectories. Naive Monte Carlo is computationally prohibitive in this regime, and constructing effective importance sampling (IS) proposals requires coordinated changes to a context-dependent chain of conditional distributions. We develop a new IS method that perturbs the original model's weights to construct the proposal. The proposal is itself a differentiably parameterized language model, enabling gradient-based search over weight space. We formulate an objective that combines a differentiable surrogate for event amplification and an adaptive regularization scheme that dynamically balances amplification against estimator stability. We evaluate our approach on $\sim$120M and $\sim$2.6B models across three event families spanning 300+ rare events as rare as $10^{-9}$, all with independently estimated reference probabilities. In our most verifiable settings, we observe that our IS estimator achieves over $800\times$ compute-weighted efficiency gains on average over naive Monte Carlo for events with probabilities lower than $10^{-7}$. Our implementation is available at https://github.com/namkoong-lab/iterative-unalignment.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13987

Communication-Learning Co-Design for Pinching-Antenna-Assisted Federated Learning in Heterogeneous Wireless Networks

Shuai Wang, Wenxuan Sun, Mingjie Shao, Wantao Jiang, Yanqing Xu, Lingxiang Li, Zhi Chen

原文摘要

Wireless federated learning (FL) suffers from two coupled bottlenecks: statistical heterogeneity across clients and communication-constrained model synchronization over unreliable wireless links. Instead of focusing on algorithmic design and/or wireless resource optimization, this paper provides a systematic study of pinching antenna (PA)-empowered FL for heterogeneous wireless networks and develops a unified communication-and-learning framework. Specifically, we integrate a segmented waveguide-enabled pinching antenna (SWAN) architecture with a heterogeneity-robust FL algorithm based on client variance reduction and dual quantization, so as to jointly mitigate communication bottlenecks and client drift. On this basis, we further develop a two-stage SWAN-assisted downlink protocol and formulate fairness-oriented uplink optimization problems for PA positioning, bandwidth allocation, and quantization design. Efficient solution frameworks are then derived for the resulting downlink and uplink optimization problems. Experimental results show the superiority of the proposed design in convergence speed and final accuracy under non-independent and identically distributed data and communication bottlenecks.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16302

Smoothed Analysis for Distributed MIS

Dipan Dey, Fabien Dufoulon, William K. Moses, Gopal Pandurangan

原文摘要

We study smoothed analysis of the distributed Maximal Independent Set (MIS) problem. Luby's algorithm computes an MIS in $O(\log n)$ rounds, while the recent $Ω(\log n/\log\log n)$ lower bound of Khoury and Schild (2026) shows that it is nearly optimal even in the LOCAL model. We ask whether small random perturbations can overcome this worst-case barrier. Given an arbitrary base graph $G$ on $n$ nodes, we consider the smoothed graph $\mathcal{G}_s=G\triangle G(n,p)$, where $G(n,p)$ is an independent random graph and $\triangle$ denotes symmetric difference. Thus, each possible edge is independently toggled with probability $p$. We establish nearly tight bounds on the smoothing needed for faster distributed MIS. For every base graph $G$, when $p=β\ln n/n$ for a sufficiently large constant $β$, a randomized LOCAL algorithm, given only $\mathcal{G}_s$, computes an MIS of $\mathcal{G}_s$ in $O(\sqrt{\log n})$ rounds with high probability. This is almost quadratically faster than the worst-case lower bound. Conversely, if $p\le(1/2-δ)\ln n/n$ for a fixed constant $δ>0$, there exists a base graph on which every randomized LOCAL MIS algorithm requires $Ω(\log n/\log\log n)$ rounds on the smoothed graph. Hence, the smoothing parameter required for this speedup is optimal up to a constant factor. These results show that smoothed analysis can overcome worst-case lower-bound barriers for distributed local graph problems.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16301

LoCoJAX: Host-Local Coordination for Small-Model RL on TPUs

Loki Chen, Gregory B. Rehm, Yang Song, Hongye Xie, Ming Lei, Chongyang Bai, Bob Kamma, Mingfu Liang 等 16 位作者

原文摘要

Reinforcement learning (RL) increasingly targets small language models, and executing these workloads efficiently across multiple accelerators requires effective coordination. On TPUs, Pathways-based RL uses a single controller to coordinate computation across hosts. This execution model is effective for large models, but its coordination computational costs are harder to amortize when small models repeatedly invoke short computations. To characterize these computational costs, we sweep dense models from 0.6B to 405B parameters on TPU v7x under a decode-heavy workload and measure how host and network computational overhead vary with model size. At 0.6B, host orchestration occupies 36% of step time and network communication 2%, whereas for large models, estimated network computational costs dominate the non-compute time. Motivated by these findings, we present Local Coordination JAX (LoCoJAX), which implements local rollout and reward processing on each TPU host with synchronous global training. In controlled comparisons against a tuned and optimized Pathways baseline on Qwen3-0.6B across three workloads and 8-64 TPU v7x chips, its colocated mode (LoCoJAX-CO) is 1.3-3.6x faster per step; on GSM8K, it completes a step faster on 8 chips than Pathways does on 64. Finally, a Ray-managed disaggregated mode (LoCoJAX-DA) overlaps rollout and training on separate hosts, and placement experiments establish when this overlap compensates for dividing devices and transferring weights: only when rollout dominates the step and receives most of the hosts. On NuminaMath-CoT at 256 chips, giving rollout 75% of hosts cuts step time by about 15% compared with LoCoJAX-CO.

cs.LG · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16300

SAGE-QA: Semantic-Aware, Grounded Evaluation for Closed-Context Question Answering on SQuAD 2.0

Chandra N Sekharan, Vishakan Umapathy, Abhishek Phadke

原文摘要

Closed-context question answering (QA) requires a system to generate an answer strictly supported by a supplied passage, or to abstain when the passage lacks adequate evidence. SQuAD 2.0 is a widely used benchmark for this setting, scored by Exact Match (EM) i.e., character-level agreement with human reference spans after lexical normalization. For generative large language models (LLMs), however, an EM failure may reflect a valid paraphrase or an alternative passage-supported answer rather than a hallucination. The best prior leaderboard EM is approximately 91%, but evaluating generative models requires separating lexical divergence from factual error. We introduce SAGE-QA (Semantic-Aware, Grounded Evaluation for Question Answering), a multi-tiered framework combining reference-based Semantic Equivalence Scoring (SES) and passage-based Re-Validation Scoring (RVS). On a fixed set of 10,000 SQuAD 2.0 validation examples, we compute EM and decision-matrix F1 from TP, TN, FP, and FN counts for zero-shot closed-weight frontier models and for open-weight models, untuned and fine-tuned. Zero-shot, Claude Opus 4.6 and Gemini 3.1 Pro reach 85.21 and 86.66 EM, outperforming untuned Llama 3 8B (56.88), gpt-oss-20b (66.28), and Llama 3.3 70B (71.39). Parameter-efficient fine-tuning adds 21.19 to 34.06 EM points, raising these models to 90.94, 87.47, and 94.24 EM, respectively. Auditing the frontier models with SAGE-QA and an external LLM judge yields dataset-wide SES scores of 92.81 for Claude Opus 4.6 and 91.67 for Gemini 3.1 Pro, and hybrid RVS scores, which combine benchmark credit with passage-based judge assessments, of 97.96 and 98.02. Fine-tuned open-weight models perform best, with fine-tuned Llama 3.3 70B surpassing the previous best known EM.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16299

Improved Lower Bounds for Mutually Orthogonal Latin Squares (MOLS) at 33 Orders

Christopher D. Rosin

原文摘要

A Latin square of order $n$ is an $n\times n$ array over symbols $\{0,1,\ldots n-1\}$, such that the same symbol does not appear twice in any row or any column. Two Latin squares of order $n$ are orthogonal if, when superimposed, they yield each of the $n^2$ ordered pairs of symbols exactly once. A set of two or more Latin squares are mutually orthogonal Latin squares (MOLS) if each pair of distinct squares is orthogonal. Let $N(n)$ be the maximum size of a set of MOLS of order $n$. For most $n$ which are not prime powers, there is a wide gap between known upper and lower bounds on $N(n)$. This has led to interest in constructing new larger sets of MOLS for explicit values of $n$, to incrementally improve the lower bounds on $N(n)$. This paper provides MOLS that improve the lower bound on $N(n)$ for 33 values of $n$ in the range from 22 through 494. The constructions were found by GPT-6 Astra, and use established techniques including difference matrices, quasi-difference matrices, orthogonal arrays, and projective planes. A highlight is 4 MOLS of order $n=22$; combined with pre-existing results, this establishes $N(n) \geq 4$ for all $n>10$.

math.CO · cs.DM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16297

Hibikino-Musashi@Home 2026 Team Description Paper

Soma Fumoto, Ryohei Kobayashi, Taiki Watanabe, Yuga Yano, Akinobu Mizutani, Naoki Yamaguchi, Ryo Terashima, Ryusei Sakakibara 等 18 位作者

原文摘要

This paper presents an overview of the integrated technologies developed for the Open Platform League at RoboCup@Home 2026 in Incheon. A vision system integrated with learning-based, training-free models enables robust object recognition for both known and unknown objects. We introduce a RoomPlan-based method that semi-automatically registers furniture geometry and labels to generate semantic maps. Additionally, a large language model-based planner interprets the instructions recognized by Whisper and generates primitive skill sequences for execution. A reservoir-computing framework serves as a lightweight temporal-processing model for action recognition. Its field-programmable gate array-based implementation enables low-power, real-time operation. This is key to removing operational time constraints for service robots. Open-source releases, including task environments, dataset tools, and navigation frameworks, enhance reproducibility, reuse, and continuous progress within the RoboCup@Home community.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02627

Recovering Missing Degrees of Freedom in Simulation-Trained Field-Level Generative Models

Masato Shirasaki, Shiro Ikeda

原文摘要

Simulation-trained generative models are emerging as a powerful framework for field-level inference in cosmology, particularly in strongly nonlinear regimes where analytical approaches become intractable. However, the finite size of training datasets often leads to variance shrinkage, indicating that part of the realization-level variability of the underlying physical process is not captured by the learned generative dynamics. In this paper, we interpret variance shrinkage as a diagnostic of missing effective degrees of freedom and introduce physics-informed latent modifications to the generative dynamics of a continuous normalizing flow. We show that these latent corrections recover a substantial fraction of the missing variability without requiring additional training simulations. Using weak-lensing field-level inference as a representative cosmological application, we further demonstrate through posterior analysis that the learned latent directions are actively exploited during inference and behave as effective nuisance parameters that absorb part of the mismatch between the training and target distributions. As a result, the model exhibits improved robustness to out-of-distribution shifts in field-level inference. Our work establishes variance shrinkage as a practical diagnostic for identifying missing degrees of freedom in simulation-trained generative models and provides a framework for recovering them through physics-informed modifications of generative dynamics.

astro-ph.CO · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.13535

Joint Communication-Control Strategy Optimization with Partially Nested Information Structures: The Linear-Quadratic Case

Haoyi You, Kaiqing Zhang

原文摘要

In this paper, we formalize a joint communication-control strategy optimization (JCCO) problem in multi-agent linear systems with quadratic costs, under the common-information-based (CIB) framework from decentralized stochastic control. For computational tractability, we focus on such JCCO problems with partially nested (PN) information structures (ISs). In particular, with a baseline communication protocol that leads to a PN IS, we establish a series of conditions under which the partial nestedness is preserved under the (additional) communication strategies to be optimized, while violating them may cause nonlinearity or non-existence of the optimal control strategies, with open-loop communication strategies. Under these conditions, we then develop a dynamic-programming-based approach to compute the optimal control strategies of JCCO with open-loop communication strategies, which yields a set of closed-form Riccati Equations. As a byproduct of independent interest, such an approach also offers a new way to solve decentralized linear-quadratic control with PN ISs and output feedback, under the CIB framework and the aforementioned conditions. Finally, we extend such an approach to JCCOs with closed-loop communication strategies, yielding a more tractable dynamic program than an infinite-dimensional CIB-belief-based one.

eess.SY · cs.MA · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11083

Who Benefits from Wikipedia's Growth Mentorship? The Role of Newcomer Information Needs

Yubo Zhou, Pengyu Chen, Ceren Budak

原文摘要

Evaluations of Wikipedia's programs for new editors usually ask whether a program works on average; we ask which newcomers benefit when their question to a mentor gets a reply. English Wikipedia's Growth mentorship system assigns every new account a volunteer mentor, and about a quarter of the questions newcomers send never get a reply. Using the first questions of 34,073 newcomers (2021 to 2026), we compare newcomers who received a reply with newcomers who did not, within groups that are similar on 164 characteristics recorded before the question (propensity score stratification). (1) Does a reply help newcomers keep editing? Yes, in the short term: in the 14 days after the reply, newcomers who received one were 1.8 percentage points (pp) more likely to edit an article (22% vs. an estimated 20% without a reply) and 1.8pp more likely to edit on two or more days. Later in the first year, the only difference that remains significant after correction for multiple testing is in the share who edit an article at least once between six months and one year after the reply (+1.5pp). (2) Does the effect depend on what the newcomer asked? Yes. We sort questions by Morrison's (1993) information-seeking types, which earlier studies measured mostly through self-reports, using a language model checked against human labels. A reply has significant effects for questions about rules and norms (+3.4pp), questions that show the newcomer's earlier editing (+3.4pp) and specific how-to questions (+2.4pp). For newcomers who do not know what to work on, a reply has no detectable effect, and the effect is 4.2pp smaller than for newcomers with other questions. (3) Which replies are followed by more editing? Among newcomers who received a reply, those whose reply came sooner or stayed closer to the topic of their question edited more.

cs.SI · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16293

Rasoi: Visual Diffs for Evaluating Structural Changes Made by Coding Agents

Riya Sahni, Aryan Kaul, Anushka Bhatnagar, Xuanming Zhang, Vivian Liu, Reya Vir, Lydia B. Chilton

原文摘要

Coding agents let developers modify software rapidly through natural language. However, as agents take on larger changes, developers can lose track of how the codebase is evolving. These edits may look correct on the surface while restructuring the system in unexpected ways: duplicating logic, changing modules outside the requested scope, removing functionality developers meant to keep, or introducing architectural drift. We investigate how visual diffs can help developers understand and evaluate the structural changes that coding agents make. We present Rasoi, a design probe that maps how a codebase's architecture evolves after each agent-made edit, letting developers inspect which architectural elements and relationships were added, modified, or removed across multiple levels of abstraction. We deployed Rasoi in a week-long study with 12 developers working on their own codebases across varied technical domains. We found that visual diffs helped developers see whether changes stayed within scope, notice unexpected restructuring, and understand how modified parts related to the system as a whole. Our findings suggest that the value of oversight tools for coding agents lies less in making developers trust the agent and more in helping developers feel confident in their own evaluation of agent-made changes. We discuss implications for visual tools that support code comprehension and human oversight in AI-assisted software development.

cs.SE · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16290

Parallel Computing Systems for the Optimal Selection of Process Equipment

Andrey B. Borisenko

原文摘要

This monograph addresses the optimal selection of equipment for chemical-engineering systems of multiproduct batch plants, a large-scale NP-hard discrete optimization problem in which, for each processing stage, a unit of standard size must be chosen so that the total capital cost of the equipment is minimized while the system remains operable and produces the required products within a given time horizon. We present the mathematical model of system operation and analyze its computational properties. We then develop a sequential branch-and-bound algorithm and its parallel versions for shared-memory systems (OpenMP), distributed-memory clusters (MPI, master-worker), and graphics processing units (CUDA), including recursive and iterative tree traversal, shared-memory optimization, and branch-divergence reduction. To overcome the exponential growth of the run time, we propose a hybrid metaheuristic in which Ant Colony Optimization finds a feasible initial solution and Simulated Annealing improves it, and we implement it on GPUs with thousands of independent threads. Finally, we propose random, constant, and multi-constant approaches to tuning the parameters of the ant colony algorithm based on statistical analysis of many parallel runs, and we study the program on GPUs of three generations and on nodes with several GPUs. Computational experiments on multi-core processors, clusters, and GPUs show that the parallel hybrid algorithm finds solutions within 0.01% of the branch-and-bound optimum in tens of seconds instead of tens of hours.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16289

Eliciting and Mitigating Specification Satisficing in Coding Agents

Ruixin Wang, Tianyi Zhang

原文摘要

Coding agents often fail to meet all requirements in the task description. In some of these failures, the agent considers an implementation choice that meets a requirement, but later decides to commit to another one that does not meet the requirement for a reason it finds acceptable. We call this specification satisficing. It is challenging to identify and mitigate specification satisficing because the agent accepts such choices for its own reasons, and existing self-correction methods are biased toward them. We propose SatisFix, which guides a coding agent to report these choices and their reasons, and then check each choice against the task description. We evaluate SatisFix on DeepSWE and SWE-bench Pro with five LLMs as backbones and two different agent frameworks, and compare it with three self-correction methods. SatisFix improves the pass@1 of the base agent by an average of 5.09% on DeepSWE and 7.06% on the hard subset of SWE-bench Pro. On DeepSWE, it outperforms the best baseline by 3.98%. Our ablation study shows that both steps of SatisFix contribute to the improvement.

cs.SE · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16287

RiskLens: Human-Informed Incident Transfer for AI Risk Evaluation

Yiliu Tang, Hangyue Zhang, Yun Huang

原文摘要

As AI capabilities advance, early-stage teams need better ways to judge whether historical incidents transfer to new products, demanding support beyond static risk checklists or one-off ideation tools. We present RiskLens, a human-AI workflow with an interactive system built around a live crawl, refreshable ranker, and extensible, human-informed schema. RiskLens analyzes 2,034 AI incidents and 10,443 AI products to rank transferable incidents using a feature schema spanning technical, operational, and user-facing attributes and supports an interface that presents ranked incident evidence, generated scenarios, risk metrics, and mitigation suggestions. In a study with 21 AI-familiar participants, users surfaced evaluation criteria that we added as 13 human-informed dimensions, including harm categories, failure mechanisms, and vulnerable groups. The feature-based model outperformed a generic baseline with an 86.2% win rate, and remained stable on a twenty-month future holdout; after human-informed schema expansion, the updated model outperformed the same baseline on 91.4% of pairs.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16285

Scaling Pêlagos: Multi-GPU Orchestration for Relay Belief-Propagation Decoding

Xulong Tang, Dionysios Diamantopoulos, Tristan Müller, Apo Kayi, Nelson Mimura Gonzalez, Muhammad Osama, Seetharami Seelam, Haralampos Pozidis 等 10 位作者

原文摘要

Fault-tolerant quantum computing requires quantum error-correction (QEC) decoders that achieve low logical error rates under real-time operating constraints. These constraints motivate heuristic decoding algorithms whose error-correction performance must be evaluated through large-scale simulation. High-throughput decoding accelerates this evaluation and the exploration of decoder parameters. Using multiple graphics processing units (GPUs) is a natural way to scale these simulations, but naively distributing syndrome batches introduces host-side preprocessing and output-collection overheads that limit scaling. To address these limitations, we extend the Pêlagos relay belief-propagation (Relay-BP) decoder with process-isolated orchestration, parallel host preprocessing, and shared-memory output. We evaluate both one-gross ($[[144,12,12]]$) and two-gross ($[[288,12,18]]$) codes in a multi-GPU environment. Specifically, for the one-gross circuit at $R=50$, eight AMD Instinct MI300X GPUs and eight AMD Instinct MI355X GPUs achieve optimized end-to-end speedups of $5.68\times$ and $6.23\times$, respectively. For the two-gross circuit, the corresponding speedups are $6.92\times$ and $7.01\times$. When throughput is calculated using GPU decoding time, the corresponding two-gross speedups are $7.58\times$ and $7.45\times$, indicating near-linear GPU scaling. We deploy Pêlagos on Lux, Oak Ridge National Laboratory's AMD Instinct MI355X-based supercomputer, as a pathfinding application for high-throughput QEC simulation. This deployment provides a concrete quantum-computing use case for the AI + HPC infrastructure supporting the Genesis Mission's scientific-discovery goals. These end-to-end measurements demonstrate a practical path to accelerating large-scale QEC simulation through efficient multi-GPU decoding.

quant-ph · cs.ET

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16284

Decisions at the end of the world

Casper van Elteren

原文摘要

International treaties are often described as tigers without teeth, failing to secure compliance or change outcomes. Cooperation also depends on how states maintain the rules they have already made -- especially where no sovereign stands above the states. Here we map more than six decades of decision-making under the Antarctic Treaty, linking submitted proposals to recorded responses and tracing how instruments act on earlier ones. Shared references tie decisions together across the agenda. Recurring revision concentrates in specialised histories, above all the management of protected areas, beyond what activity, popularity and reference age predict. Periodic administrative reviews retire outdated instruments in bulk, joining otherwise separate histories without coordinating policy across subjects. Binding Measures build overwhelmingly on earlier Measures, with little direct succession from non-binding Resolutions. Distinguishing these layers shows how a shared rule system is sustained by consensus, and provides a basis for comparing cooperation across global commons.

physics.soc-ph · cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16282

Robot Navigation and Obstacle Avoidance from Randomly Positioned External Cameras

Raul Steinmetz, Jair Augusto Bottega, Ayanori Yorozu, Akihisa Ohya

原文摘要

We study robot navigation in indoor environments from fixed exocentric cameras (infrastructure-mounted, external to the robot). We show that standard Proximal Policy Optimization (PPO), which succeeds with onboard cameras, fails to learn this navigation end-to-end in our setup. This failure results from the perspective mismatch between exocentric observations and egocentric action and goal formulations, which is too complex to resolve from the reward signal alone. To address this, we propose Viewpoint-Invariant Navigation from Exocentric Cameras (VINE). VINE uses an obstacle map estimation network, trained with supervised learning, that takes exocentric images, camera pose, robot pose, and room dimensions as input and produces a viewpoint-invariant obstacle map. This map can be used either by a PPO policy or by a planner such as A*. VINE's map estimation performs better than perspective mapping approaches, handles cameras placed across a wide range of positions and orientations without view-specific tuning, requires only approximate camera pose estimates (noise with a standard deviation of 30cm per axis in x, y, and z and about 20° of rotation error on average), is robust to intrinsic variation, and extends to two-camera setups with partial coverage. Despite operating from cameras randomized in position during evaluation, VINE surpasses the navigation success rate of PPO with an onboard camera in simulated environments.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16275

Demo: Matter-Based IoMT Telemetry and Security Analysis

Muhammad Hassan, Masooda Bashir, Carl Gunter

原文摘要

Connected health devices increasingly support health monitoring outside clinical settings, making secure and accessible physiological data important for patients and caregivers. However, device-specific applications and interfaces complicate the integration of readings from different devices. The Matter standard provides standardized device commissioning and protected communication, but its cluster library does not represent the health attribute measurement. We present a preliminary demo study of a Matter-based IoMT testbed with a specific HealthVitals cluster. Implemented in the connectedhomeip SDK on a Raspberry~Pi~5, the cluster exposes simulated heart rate, blood oxygen saturation, and body temperature. We examine commissioning, attribute retrieval, communication under simulated packet loss, and operational traffic through a reference controller. The study demonstrates that this custom telemetry model can operate within the tested Matter configuration without modifying commissioning or secure-session mechanisms. The resulting testbed gives researchers and device developers a concrete platform for investigating health-oriented extensions. It also supports exploration of local telemetry integration that could help patients and caregivers access readings across connected devices.

cs.AR · cs.ET · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16274

Distribution-First Framework for Learning Risk-Sensitive Individualized Treatment Rules

Zhen Fang, Guanting Chen, Yufeng Liu

原文摘要

Learning a risk-sensitive individualized treatment rule (ITR) with patient covariates is challenging when the treatment criterion depends on the full conditional outcome distribution rather than only its conditional mean. Existing methods are largely criterion-specific, making it difficult to compare multiple treatment criteria within a common pipeline, such as quantiles, conditional value-at-risk (CVaR), and complex preference-based objectives such as cumulative prospect theory. We propose a distribution-first framework for risk-sensitive ITR learning that first estimates treatment-specific conditional outcome distributions and then constructs the target ITR by plug-in criterion evaluation. The proposed framework unifies treatment rule learning across multiple criteria and accommodates hard-to-optimize objectives within the same estimation pipeline. On the theoretical side, it yields a general error propagation result for locally Hölder-stable criteria, linking first-stage distribution estimation error to downstream criterion estimation and treatment regret, together with sharper regret bounds under standard margin conditions. Empirically, we show that the proposed framework outperforms direct criterion-specific methods in multiple settings and further evaluate it on the ACTG175 HIV clinical trial, a randomized study comparing several antiretroviral regimens in HIV-infected adults.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.12125

CARE-PO: Controllable Tool-Call Efficiency for LLM Agents

Xiaoyang Ye, Xiao Yao, Hairui Zhu, Yiying Yang, Bei Gan, Jinliang Lu, Tengjin Weng, Yuan Yao 等 9 位作者

原文摘要

External tool calls are a major running cost of an LLM agent, while its training objectives align almost exclusively with task completion. In a step-level diagnosis of Qwen3.5 trajectories at two model sizes on GAIA, a long-horizon tool-use benchmark, we find that most contain removable calls, at a similar rate at both sizes. Some methods add an efficiency reward to the objective to reduce the call count, but introduce problems of their own: the anchor they price calls against is computed from the policy's own rollouts (e.g., the group's shortest correct rollout) and so moves with the policy; training can drift toward a collapsed policy that issues no tool call at all, a degeneracy seen in published reports and in our 27B baseline; and a training run delivers a single operating point, so the user cannot trade efficiency against capability. We present CARE-PO (Controllable Anchored Relative Efficiency Policy Optimization) to mitigate these problems. The efficiency reward prices calls against a reference budget anchored to the task's own difficulty and fixed before training. Injecting a successful trajectory from the training data into each online GRPO rollout group and charging for evidence a trajectory never gathers ease the drift toward a collapsed call count. Shaping the cost curve by an effort tag in the prompt yields an efficiency and a capability operating point from a single training run. On GAIA, five of the six operating points across three model sizes improve on their base model in success rate and call count at once. On four held-out multi-hop benchmarks the result is size-dependent: at 27B and 4B success rate is held or raised at a lower call count, while at 9B both operating points trade success rate for fewer calls. The call count remains well above zero throughout training at all three sizes.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.16272

A Binary Search Tree with the Unified Bound

Yang Cao, Haoran Qi, Hanzhi Wang

原文摘要

We give a deterministic online binary search tree (BST) that satisfies the unified bound of Iacono [SODA 2001], which subsumes the working-set and dynamic-finger bounds: it serves every sequence $X$ of $m$ searches on $n$ keys at total cost $O(m+\mathrm{UB}(X))$, and every single search at cost $O(\log n)$. Previously, no BST was known to satisfy the unified bound, even offline. As a consequence, the dynamic optimality conjecture implies Iacono's conjecture that splay trees satisfy the unified bound. We also show that the unified bound is $O((m+n)(1+\log c_σ))$ on every sequence of searches that avoids a fixed permutation $σ$, where $c_σ$ is the Füredi-Hajnal limit of $σ$. Hence every BST that satisfies the unified bound, including ours, serves such sequences at this cost. For permutations this bound is tight, and it improves the previous upper bound $O(nc_σ^2)$ on the optimal cost, due to Berendsohn, Kozma and Opler [STOC 2024]. Finally, we extend the tree to support insertions and deletions: a sequence $X$ of $m$ operations, starting from $n_0$ keys, costs $O(n_0+m+\mathrm{UB}(X))$ in total, where $\mathrm{UB}$ is now the extension of the unified bound to insertions and deletions by Bădoiu, Cole, Demaine and Iacono [TCS 2007]. Hence, if splay trees are dynamically optimal for searches, insertions and deletions, then they serve every such sequence at this total cost as well.

cs.DS

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新论文 · v1arXiv:2611.16271

Hierarchical Decoding for Quantum Error Correction

Xulong Tang, Narges Alavisamani, Joan Camps, Matthew Cha, Elise Jennings, Jack O'Connor, Humberto Munoz Bauza, Josh Izaac 等 21 位作者

原文摘要

Fault-tolerant quantum computing requires classical decoders that can keep up with syndrome data streams and supply correction information and logical measurement outcomes to the quantum control system. Meeting these requirements at scale requires high throughput while controlling the end-to-end latency from readout to usable correction. In this paper, we organize decoding algorithms, execution strategies, and code constructions using common classifications and examine their performance and trade-offs under the reported evaluation conditions. We distinguish decoder-level hierarchy, where multiple decoding stages or resources cooperate to decode a single code, from code-level hierarchy, where the code construction itself introduces multiple decoding levels. We first review single-code hierarchical decoders and their cascaded predecoding and windowed, streaming, and distributed implementations. We then study hierarchical and concatenated codes, focusing on how decoding is coordinated across code levels through hard decisions, soft reliability information, or syndrome information. We also examine how qubit modalities shape decoding workloads and timing requirements, and review how different decoding platforms meet these requirements, including central processing units (CPUs), graphics processing units (GPUs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and cryogenic platforms. By assessing design trade-offs, this paper clarifies connections across the literature and provides a shared foundation for QEC design exploration. It also identifies gaps in performance reporting and system integration to guide future research toward practical quantum error correction.

quant-ph · cs.ET

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新论文 · v1arXiv:2611.16270

SMIC: Shared Multiscale Input-Reference Conditioning across Transformer Layers for Natural Language Inference

Xin Zhang, Victor S. Sheng

原文摘要

Natural Language Inference models can be sensitive to localized lexical changes. We introduce Shared Multiscale Input-Reference Conditioning (SMIC), which constructs a single input-dependent multiscale reference and reuses it across Transformer layers. Each layer applies its own multiscale transformation to the shared reference and injects the resulting conditioning feature before the attention update. We instantiate SMIC in a token--context Transformer (TCT) and conduct from-scratch comparisons against decoder-based and Multi-Layer Perceptron (MLP) baselines across all three ANLI rounds. TCT-SMIC attains the highest observed ten-seed mean accuracy on all three ANLI rounds. The ablations further support the contribution of SMIC. We evaluate a real-valued Llama-style plug-in and extend deletion/insertion diagnostics to pretrained ModernBERT encoder and Llama decoder models with and without SMIC. Across the evaluated perturbation protocols, TCT-SMIC exhibits comparatively low pooled-attention redistribution and smaller signed accuracy changes. We further establish a finite-sample bound on average subset-mass redistribution in terms of the mean Jensen--Shannon divergence between the restricted and renormalized attention distributions.

cs.CL · cs.AI · cs.LG

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新论文 · v1arXiv:2611.16269

FlowIR: Depth-driven Rectified Flow for Resolving Thermal Ambiguity in Infrared Super-Resolution

Xingyue Zhu, Qinyun Mei, Zhexi Min, Yang Zou, Xingyuan Li, Zhiying Jiang, Jinyuan Liu

原文摘要

Distinguishing different heat sources in low-resolution infrared (IR) images is inherently difficult, as multiple thermal emitters often collapse into ambiguous and overlapping responses. Existing super-resolution methods hallucinate structures from such ambiguous 2D evidence, leading to thermal drift, boundary merging, and unreliable restoration under infrared degradations. We propose FlowIR, a depth-driven rectified flow framework that resolves thermal ambiguity by injecting 3D geometric cues into the generation process. Pseudo-3D depth lifting and depth-guided 3D convolutions disentangle overlapping foreground objects, while cluster-wise inter-attention suppresses cross-source feature contamination. A Thermal-Geometry Consistency Loss further enforces geometric alignment during refinement. Built upon a lightweight rectified flow backbone, FlowIR restores IR images in only a few steps, producing sharply separated, degradation-robust, and geometrically faithful high-resolution results.

cs.CV

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新论文 · v1arXiv:2611.16267

Causals: An Open Knowledge Graph for Research on Causal Reasoning

Miaozhe Lin, Qiongzheng Lin, Zhongwei Liang, Zijun Liu

原文摘要

Applying causal knowledge to a new event requires connecting the event to relevant entities and the relationships through which its consequences may unfold. We present Causals, an open knowledge graph for research on this form of reasoning. Its initial release, constructed from news reports, contains 369,184 nodes and 1,272,418 edges linking reported events, organizations, commodities, financial instruments, and other entities. Structural relationships, observations, and attributed causal claims retain distinct semantics and links to source records. We describe the construction and representation of the resource and characterize its connectivity and multi-hop retrieval properties. Causals provides shared relational knowledge for studying how language models retrieve relevant context and apply mechanisms to particular events. The dataset is available under CC BY 4.0 at https://causals.org/, and we invite contributions to its ontology, records, and evidence.

cs.AI

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版本更新 · v2arXiv:2608.19858

Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection

Jialun Zheng, Hanchen Yang, Jiannong Cao, Yankai Chen, Yuanjing Feng, Philip S. Yu

原文摘要

Generalizable dynamic graph anomaly detection (DGAD) enables pretrained detectors to identify anomalies in unseen target domains without costly retraining. However, existing methods often fail for two reasons. First, they mainly rely on domain-agnostic patterns and miss domain-specific patterns that keep evolving. Second, they assume access to the full target domain data, whereas in more practical online test-time adaptation settings, target data arrive sequentially in unlabeled chunks. To address these limitations, we formulate online test-time adaptation for generalizable DGAD and propose OTTA-DGAD. OTTA-DGAD first extracts dynamic prototypes, i.e., evolving representations of normal and anomalous patterns, from temporal ego-graphs and stores them in a memory buffer. The buffer selectively retains general patterns shared across the source domains used for pretraining while incorporating new patterns from the target domain. An anomaly scorer then compares incoming edge representations against these prototypes to identify both general and domain-specific anomalies. During adaptation, OTTA-DGAD updates the memory buffer using reliable pseudo-labels identified through confidence-based detection. It further enriches each target chunk with relevant representations retained from previous chunks, compensating for information loss resulting from the sequential arrival of data. Extensive experiments under strict test-then-adapt OTTA settings demonstrate state-of-the-art performance on ten real-world datasets from diverse domains.

cs.LG

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版本更新 · v2arXiv:2609.33663

LieDiscover: Adaptive Symbolic Library Construction for Explicit Open-form Symmetry Discovery

Xinxin Li, Jianming Ma, Xingyu Cui, Da Li, Juan Zhang, Junping Yin

原文摘要

Discovering symmetries from observational data provides interpretable insights into dynamical systems and priors for scientific machine learning. However, existing symmetry discovery methods face two limitations. Many methods represent symmetries implicitly, making it difficult to obtain explicit infinitesimal generators that can serve as physical priors for learning dynamical systems. Meanwhile, methods that explicitly identify generators often rely on predefined function libraries, limiting their ability to capture complex symmetries involving high-order polynomials or transcendental functions. To address these limitations, we propose LieDiscover, a framework that explicitly identifies infinitesimal generators through an iterative symbolic search process that dynamically expands the symbolic function library to capture complex functional forms. Specifically, at each search step, we design an encoder-decoder architecture to generate a candidate symbolic expression. An adaptive singular value decomposition (SVD)-based evaluation is introduced to assess how each candidate contributes to the recoverable symmetry subspace. The resulting evaluations are incorporated into step-wise rewards to guide reinforcement learning toward discovering richer symmetry structures with concise symbolic representations. Experiments on eight partial differential equation (PDE) benchmarks demonstrate that LieDiscover recovers explicit generators involving higher-order polynomial, exponential, and trigonometric terms, while achieving more accurate symmetry subspace recovery and improved robustness to noise. Furthermore, the recovered generators enable effective symmetry-based data augmentation, improving the accuracy of downstream neural PDE solvers.

cs.SC · cs.LG

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版本更新 · v2arXiv:2610.10498

EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution

Python Song, Zhixuan Liang, Kelsey Fu, Mengdi Wang, Junfeng Yang, Shilong Liu

原文摘要

Robot foundation models provide strong visuomotor control, yet their performance can degrade when object positions or task instructions change. Further improvements often require post-training on substantial robot data, which can be costly to collect through methods such as teleoperation. Agentic harnesses can adapt around the model, but current self-evolving harnesses use robot trials inefficiently when deciding which code and skill changes to pursue. We introduce EmbodiedRSI, a self-evolving agentic harness that autonomously decides where to explore next and turns the resulting physical interaction into improved code and skills. EmbodiedRSI realizes this through a Fast-Slow Dual-System Architecture, in which competing code and skill hypotheses are maintained in a Hypothesis Graph. Value-of-Information Experiment Selection chooses physical experiments that can distinguish these hypotheses. Their outcomes guide Code-Skill Co-Evolution. The Slow System builds Hierarchical Memory, and Reward-Grounded Memory Learning selects effective memory according to their value for later Fast-System improvement. On RoboCasa365, EmbodiedRSI reaches 77.0% overall success and 71.3% on Composite-Unseen, compared with 40.1% for the best baseline. EmbodiedRSI also reaches 86.8% overall success on LIBERO-Pro. Beyond benchmark performance, EmbodiedRSI transfers zero-shot to real-world robot, achieving 71.3% overall success across multiple challenging tasks.

cs.AI

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新论文 · v1arXiv:2611.16265

Rectified Looped Transformer

Musheng Li, Jiawei Zhang, Ziyuan Liu, Yuantao Gu

原文摘要

Looped Transformers increase effective depth by repeatedly applying a shared block, offering a parameter-efficient approach to language modeling. However, most existing works directly reuse each loop's output as the next's input, which can lead to hidden-state scale growth and perturbation amplification as recurrence deepens. We propose Rectified Looped Transformer (RLT), which redesigns feedback between loops using an interpolation schedule inspired by flow-based modeling. At each loop, RLT forms the next input by linearly interpolating between the current state and its prediction. Such interpolation distributes state changes across loops, encouraging a smoother trajectory from the initial input to the final output. Theoretical analysis proves that, under appropriate assumptions, RLT keeps state magnitudes uniformly bounded across loops and reduces the worst-case perturbation amplification. We evaluate RLT on language modeling corpora and downstream benchmarks. At matched training and inference depths, RLT consistently achieves lower perplexity than a standard looped Transformer, while exhibiting greater robustness to input perturbations and variance in inference depth. Consistent with the theoretical results, latent trajectory analysis shows that RLT maintains more stable hidden-state norms across loops, which also facilitates extrapolation-based decoding strategies. Notably, RLT achieves these gains without requiring additional trainable parameters or extra recurrent block evaluations, thus providing a simple yet more stable feedback mechanism for looped Transformers.

cs.LG · cs.AI

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