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arXiv 最近完成扫描：2026-11-15 10:52 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

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版本更新 · v2arXiv:2602.02505

Learning-augmented smooth integer programs with PAC-learnable oracles

Hao-Yuan He, Jiang-Tian Xue, Ming Li, Zhi-Hua Zhou

原文摘要

We study an end-to-end learning-augmented framework for smooth integer programs, a broad class of Boolean polynomial optimization problems including MAX-CUT and MAX-k-SAT. An oracle learned from sampled instances predicts a full Boolean assignment for each unseen instance. For fixed-degree objectives, our polynomial-time pipeline, LEAP, linearizes the objective around the predicted assignment, solves an error-budgeted linear program, and rounds the fractional solution. We establish two guarantees. First, for any prediction, the approximation loss of LEAP degrades gracefully with prediction error: it is controlled by an error footprint that quantifies how much mispredicted coordinates can perturb the objective's local coefficients. Second, we bound the pseudo-dimension of the LEAP-induced cost class, yielding a PAC-style guarantee for learning an oracle from a predictor class via empirical risk minimization on instances from an unknown distribution. Together, these results give an end-to-end bound that separates the expected footprint loss of the best oracle in the class from the statistical error of learning. In experiments on MAX-CUT and MAX-SAT, LEAP substantially repairs imperfect predictions, empirical risk minimization selects effective oracle candidates, and the pipeline scales to application benchmarks with up to two million clauses.

cs.DS · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39582

Kinematic-Tactile Aligned Dexterous Retargeting with Contact Wrench Space Alignment

Xinyuan He, Ziheng Xu, Yueyuan Chen, Yufan Ye, Yuanshuo Tan, Huiming Pan, Jianjun Meng, Shuo Jiang 等 9 位作者

原文摘要

Human demonstrations provide a natural and scalable source of data for learning dexterous manipulation policies. However, existing retargeting methods primarily transfer human kinematics, while the tactile information underlying physical interaction is rarely preserved across embodiments. To address this limitation, we present a hierarchical three-stage framework for kinematic-tactile aligned dexterous retargeting. We use object-centric contact wrench space as a physical representation for tactile alignment between human and robot hands. The framework first aligns hand kinematics and contact geometry, and then constructs a human wrench support profile from tactile forces and contact geometry. Robot fingertip force targets are subsequently optimized to cover the human wrench support and converted into joint position corrections, yielding directly replayable robot hand trajectories. We evaluate the framework in simulation and real robot experiments across three pouring conditions. In the real robot experiments, our method achieves a 96.7\% replay success rate and higher wrench transfer fidelity than representative retargeting baselines. The framework enables physically grounded transfer of kinematic and tactile information from human demonstrations to robot hands, providing a path toward scalable learning of contact-rich dexterous manipulation.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37509

Ego-CEVR: Learning Cross-Embodiment Visual Representations from Human Egocentric Videos

Keru Zhou, Zhirui Fang, Xinrun Xu, Yaopeng Lou, Zeyuan He, Zihao Zhu, Xinze Niu, Chen Wang 等 22 位作者

原文摘要

As egocentric video becomes increasingly available for embodied learning, an important research question is how to turn this low-cost, scalable source of data into robot manipulation capabilities. Egocentric videos naturally provide rich context for high-level task understanding, while real-robot demonstrations with wrist observations and action annotations provide supervision for fine-grained action alignment. We present Ego-CEVR, a data-native framework for hierarchical visual representation learning that assigns distinct learning objectives to global and interaction representations. The global module learns object relations, task state, and temporal progress through future prediction on egocentric video. The Interaction module interprets wrist observations in global context and learns local alignment and contact cues through robot action supervision. Separate residual paths expose both representations to the action policy, preserving direct access to task context alongside fine-grained execution information. Ego-CEVR supports independent pretraining of its global video module on large-scale human egocentric videos and videos from diverse robot embodiments, without requiring robot action labels or paired wrist observations.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.37029

LongSpark: Efficient speculative decoding with a fixed-cost parallel drafter

Hao-Yuan He, Peng-Fei Liu, Chen Chen, Si Shen, Quan He, Runzhou Liu, Xinqi Li, Zhenhua Dong 等 9 位作者

原文摘要

Speculative decoding accelerates autoregressive inference by verifying multiple draft tokens in a single target forward pass. However, as the context grows, existing state-of-the-art drafters become increasingly expensive, eroding the very efficiency advantage they are designed to provide. We argue that this scaling is unnecessary. A standalone language model must grow with its prefix because it is solely responsible for every token it produces. A drafter, by contrast, only proposes candidates; the target catches and corrects every error before any token is committed. The drafter's decoding cost can therefore be made entirely independent of the prefix length. We introduce LongSpark, a block-diffusion drafter that achieves this by extracting fixed-size, multiscale views from the target's verification pass, thereby eliminating the need for a growing persistent state. Extensive evaluations demonstrate that LongSpark achieves state-of-the-art end-to-end efficiency across multiple model scales and realistic serving conditions. Notably, it delivers the lowest time-per-output-token on long-context tasks while reducing the drafter's context state by several orders of magnitude.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.28876

Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents

Liqin Ye, Haorui Wang, Fardin Ahmed, Rongzhi Zhang, Yuan He, Ziyuan Lin, Yanbin Yin, Jing Peng 等 11 位作者

原文摘要

Forecasting requires large language model (LLM) agents to gather evolving evidence, revise their beliefs, and make calibrated predictions over time. Yet evaluating and improving these capabilities is challenging because real-world events unfold continuously and outcomes may take months to resolve. We introduce Forecast-Dojo, a replayable environment that reconstructs resolved prediction-market events as sequences of dated forecasting steps, enabling reproducible evaluation, longitudinal analysis, and training from agent interactions. It comprises 1,568 Polymarket events split temporally into training and evaluation sets, alongside 18.8 million dated news articles. Across 12 models, research tools consistently improve forecasting performance, and greater capture of new evidence is associated with larger improvements over time. Nevertheless, all models trail historical market forecasts, while carrying a belief notebook across dates reduces research costs without consistently improving forecast quality. We supervise fine-tuned Qwen3-30B-A3B-Thinking-2507 on collected trajectories from Qwen3-235B-A22B-Thinking-2507 to demonstrate the environment's training utility. The student's accuracy is improved from 34.8% to 42.8% on the evaluation set.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.22684HF 社区推荐

AlphaBet: Reinforcement Learning for LLM Event Forecasting Agents

Liqin Ye, Rongzhi Zhang, Xin Liu, Zheng Li, Yuan He, Qingyu Yin, Priyanka Nigam, Chao Zhang

原文摘要

Forecasting an unresolved event requires finding relevant evidence and deciding how strongly it supports each outcome. Yet outcome-based training of Large Language Model (LLM) forecasters often begins with preassembled evidence at a single prediction date, leaving evidence acquisition outside the learning loop. We introduce AlphaBet, an agentic reinforcement learning (RL) framework that trains a single LLM policy to search, reason, and report probabilities across multiple pre-resolution dates. Each dated forecast is scored by a proper scoring rule against the eventual outcome, allowing one resolved event to supervise decisions under different information states. We decompose forecasting regret into an evidence-acquisition gap and a probability-reporting gap, and show that Group Relative Policy Optimization (GRPO)'s standard-deviation normalization can distort the reporting target under multi-class proper scores. On GLM-4.5-Air, AlphaBet reduces the Brier score from 0.731 to 0.626 on Forecast-Dojo and from 0.612 to 0.528 on FutureX. Controlled ablations attribute these gains to multi-date training and show that they come from improvements in both information acquisition and probability reporting. Our empirical results validate that GRPO's std normalization degrades proper-scoring-rule performance while leaving top-outcome accuracy largely unchanged.

cs.LG · cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.962ACL Anthology · ACL 2026

ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis

Runkai Li, Jia Xiong, Xiuyuan He, Jieru Zhao Shanghai Jiao Tong University, Jiaqi Lv, Haowen Fang Synopsys Inc., Lei Qi Southeast University, Xi Wang Southeast University

原文摘要

High-Level Synthesis (HLS) improves IC development productivity by enabling hardware design from C-like languages. However, strict coding constraints and design-specific optimizations limit its widespread adoption. While recent efforts employ large language models (LLMs) to assist HLS design, they often struggle with synthesizability rules and directive semantics. To this end, we introduce ChatHLS, a multi-agent HLS design framework that leverages specialized LLMs for automated debugging and directive tuning. ChatHLS incorporates an adaptive error case expansion mechanism, combined with a reasoning-to-instruction analysis method to accurately diagnose HLS errors. To optimize hardware performance, it enables QoR-aware reasoning to learn the impact of HLS directives on the quality of results (QoR). Experimental results demonstrate that ChatHLS outperforms Gemini-3-pro with a 32.6% relative improvement in debugging, while achieving significant speedups across various HLS kernels and neural network accelerators. These results underscore the potential of ChatHLS for agile hardware development.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表