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新论文 · v1arXiv:2611.13039

Robo-MLT: Dual-System Robotic Manipulation for Long-Horizon Medical Laboratory Tasks

Zequan Zeng, Kai Wang, Delvin Ce Zhang, Huaping Liu, Zhipeng Yuan, Junqin Yang, YunLong Zhang

原文摘要

Reliable medical laboratory automation requires robots to complete entire workflows in protocol order while handling changes during execution. A robot may execute each motion correctly yet fail the workflow by confusing visually similar stages or stalling after human intervention. We present Robo-MLT, a dual-system framework that couples explicit workflow monitoring with learned action generation. A vision--language planner composes an ordered atomic-skill sequence from the protocol and the initial workspace observation. A multiframe execution monitor detects when the active skill has completed or become infeasible, then advances the workflow or triggers replanning. A skill-conditioned vision--language--action policy generates the manipulation trajectories, and an asynchronous runtime coordinates it with the monitor. We evaluate Robo-MLT against four policy baselines and a VLM-based monitor baseline on blood gas analysis and blood sample tube sorting, with 50 nominal configurations per method and 20 sample-removal configurations. Robo-MLT achieves task success rates of 82\% and 70\%, exceeding $π_{0.5}$ by 30 and 22 percentage points, respectively. After an operator removes the sample being handled, the system revises the remaining plan and continues processing the other samples. These results indicate that, under the evaluated conditions, explicit workflow monitoring makes long-horizon laboratory manipulation more reliable and recoverable.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.16189

Searching Videos as Trees: Self-Correcting Agents for Grounded Long Video QA

Ce Zhang, Ziyang Wang, Yulu Pan, Oluwatumininu Oguntola, Pranav Wagh, Qiyu Wu, Hiromi Wakaki, Mohit Bansal 等 9 位作者

原文摘要

Grounded long-video question answering (Grounded LVQA) requires answering a question about a long video while localizing the short evidence interval that supports the answer. Recent agentic methods frame this task as multi-turn exploration with a single crop_video(start, end) action, which supports coarse-to-fine narrowing but provides no primitive for fine-to-coarse backtracking. As a result, these agents typically converge prematurely and cannot recover from an early mistake. We propose VideoTreeSearch (VTS), a framework that casts grounded LVQA as iterative self-correcting search over an adaptive temporal tree. VTS constructs a non-uniform tree through adaptive temporal segmentation so that each node corresponds to a semantically coherent segment, and trains an agent to navigate the tree through four discrete operations: zoom_in, zoom_out, shift, and answer. These operations expose backtracking and recovery as explicit, learnable primitives rather than implicit behaviors. To train this navigation, we introduce a trajectory synthesis pipeline that produces multi-step paths through the tree, including deliberate detours into incorrect branches followed by recovery. We use these trajectories for supervised fine-tuning, followed by reinforcement learning with grounding and answer-accuracy rewards. On three Grounded LVQA benchmarks (CG-Bench, Haystack-LVBench, Haystack-Ego4D), VTS outperforms the strongest prior multi-turn cropping agents by +12.5 mIoU on CG-Bench and +7.4 T-F1 on Haystack-Ego4D. The learned policy also transfers to general long-video QA, surpassing all prior multi-turn cropping agents on Video-MME, MLVU, and LVBench by up to +7.1 accuracy points. Ablations confirm that self-correcting hierarchical search is the central mechanism behind these gains: removing either adaptive descent or explicit backtracking substantially degrades performance.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.11420

Context-Aware Multimodal Claim Verification in Spoken Dialogues

Chaewan Chun, Delvin Ce Zhang, Dongwon Lee

原文摘要

Spoken factual claims often occur within multi-turn conversations, where surrounding dialogue can provide context unavailable from the claim alone. Yet most fact-checking research evaluates isolated text, leaving conversational audio under-studied. We introduce MAD2, a synthetic Multi-turn Audio Dialogues benchmark for spoken claim verification with 1,000 two-speaker dialogues, 1,230 sentence-level check-worthy candidate annotations, and approximately 10 hours of audio. We also propose calibrated multimodal fusion of a context-aware audio encoder and a dialogue-aware text model. Adding dialogue context improves verification across settings, although the gains vary by scenario. Past-only context often approaches local offline performance, suggesting its usefulness when future context is unavailable. Fusion achieves its highest mean advantage over text with full-dialogue context, but does not consistently or significantly outperform text across settings. Under full-dialogue context, exploratory subgroup results show greater performance variation across dialogue scenarios for text and fusion, but across spread styles for audio.

cs.CL · cs.SI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.16767

When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms

Chaewan Chun, Delvin Ce Zhang, Dongwon Lee

原文摘要

Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and live streams. With millions of shows and hundreds of millions of listeners, audio platforms are now a major channel for misinformation. Yet existing fact-checking pipelines are mostly designed for written claims, overlooking the unique properties of spoken media. We argue that audio misinformation is not merely textual content with transcripts: it is structurally different because it is both spoken - carrying persuasive force through prosody, pacing, and emotion - and conversational - unfolding across turns, speakers, and episodes. These dual properties introduce verification difficulties that traditional methods rarely face. This position paper synthesizes evidence across modalities and platforms, examines datasets and methods, and highlights why existing pipelines fail on audio. We argue that advancing fact-checking requires rethinking verification pipelines around the spoken and conversational realities of audio.

cs.CL · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.04452

TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audio

Chaewan Chun, Meruyert Aristombayeva, Jiyoung Choi, Mahjabin Nahar, Delvin Ce Zhang, Dongwon Lee

原文摘要

Modern misinformation is often heard before it is read, yet fact-checking systems are still evaluated mainly on clean written claims. Spoken dialogue remains different even when systems operate on transcripts: claims may be distributed across speakers and turns, depend on prior context, and become harder to verify when Automatic Speech Recognition (ASR) errors distort the available text. Prior spoken dialogue fact-checking resources are small, English-centric, or focused on annotation rather than end-to-end benchmarking, leaving no large multilingual benchmark with paired speech and turn-level labels. We introduce TRILOGUE (TRIlingual spoken diaLOGUE fact-checking), a large-scale trilingual benchmark of source-grounded spoken dialogues in English, Russian, and Kazakh. It contains nearly 12K dialogues, 187K turns, and 390 hours of paired audio with ASR transcripts and word-level timestamp alignments across all three languages, including nearly 5K human-recorded Russian and Kazakh dialogue files. TRILOGUE supports claim check-worthiness detection, source-article evidence retrieval, and claim verification with claim-only, gold-evidence, and retrieved-evidence inputs. Baselines show that ASR degradation and cross-lingual transfer remain challenging, especially for Kazakh, while retrieved source evidence substantially narrows the gap to gold-evidence verification.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04565

Curriculum unlearning in humans and neural networks

Jirko Rubruck, Alice Zhang, Andrew Saxe, Christopher Summerfield

原文摘要

During learning, humans often benefit from curricula that teach simple rules before exceptions. However, we lack a computational explanation for this phenomenon. Here, using a canonical behavioural task and modelling with neural networks, we develop and test a theory for why simple-to-complex curricula succeed. We show that during learning, both humans and neural networks show simplicity biases, a tendency to learn simple rules that are underfit to the data. In neural networks this bias is accompanied by compression of neural representations. Simple-to-complex curricula help both types of learners overcome this compression, developing representations (and policy) with appropriate dimensionality for the task. We test and confirm a further prediction from this theory: learners that have been pre-exposed to simple tasks and with more compressed initial representations benefit more from curricula. These findings suggest that curricula help with ``unlearning'' of simple rules.

cs.LG · q-bio.NC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04278

Unmitigated? How are the world's largest companies responding to AI risks?

Alexander K. Saeri, Jess Graham, Michael Noetel, Peter Slattery, Kun Zhao, Eunice Zhang, Erin Howard, Neil Thompson

原文摘要

AI is introducing new risks to large companies and amplifying existing risks, but whether companies are addressing the AI risks they are exposed to is largely untested. We investigated what outsiders can observe: whether companies publicly acknowledge AI risks. We coded public documents from 200 of the largest companies worldwide, collected in early 2026, against the 24 AI risks in the MIT AI Risk Repository, and compared acknowledgements with expert judgements of risk severity, sector vulnerability, and AI role responsibility from a companion Delphi study of 272 AI risk experts. Of the 100 largest companies, 32% did not publicly acknowledge any AI risks. Of the largest 1000 companies, we estimate that 62% [95% CI: 54%, 70%] did not publicly acknowledge any AI risks. Where companies did acknowledge risks, five accounted for over half of all acknowledgements: discrimination, weapons & cyberattacks, fraud & scams, loss of privacy, and devaluation of human creativity. Several risks that experts judged most severe were almost absent: almost no company acknowledged power centralization (concentration of power among those who control advanced AI), competitive dynamics (racing to deploy AI at the expense of safety), or dangerous capabilities. Companies in sectors experts judged more vulnerable to a risk were more likely to acknowledge that risk. But even companies in sectors judged 'extremely vulnerable' to a risk usually did not acknowledge it. Companies with roles experts judged most responsible for addressing a risk were no more likely to acknowledge it than other companies. General-purpose AI developers acknowledged AI misalignment (15%) far less often than discrimination (85%). These findings provide an empirical baseline for the design of AI risk disclosure requirements.

cs.CY

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1124ACL Anthology · ACL 2026

WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models

Rui Wang, Ce Zhang Carnegie Mellon University, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang Tencent AI Lab 等 13 位作者

原文摘要

The hallmark of Deep Research agents lies in compositional reasoning, the capacity to aggregate distributed, heterogeneous information into coherent logical insights. However, current agentic systems are often retrieval-heavy but reasoning-light, where success is predominantly determined by simple entity-seeking rather than the multi-step aggregation of scattered evidence. To address this, we propose a data synthesis pipeline WebAggregator, designed to shift the agentic paradigm from retrieval-centric to compositional aggregation. Our approach first employs Proactive Explorer to collect interconnected knowledge, then Compositional Logic Proposer to weave knowledge into complex questions using over 12 composition guidelines derived from a rigorous deconstruction of the Deep Research problem setting. Fine-tuning on this corpus fundamentally transforms agent behavior, fostering deliberate composition reasoning and reduced tool redundancy. The resulting WebAggregator-32B surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench. To address the lack of benchmarks that emphasize both reasoning and retrieval, we introduce the WebAggregatorQA testbed, which reveals that even with perfect retrieval, top-tier models still underperformed. These results demonstrate that compositional reasoning, not retrieval, is the true performance ceiling for next-generation research agents.

cs.CL · cs.AI

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1414ACL Anthology · ACL 2026

ArgGenBench: Benchmarking the Complex Controlled Argument Generation Capability of Large Language Models

Bojun Jin, Jianzhu Bao Nanyang Technological University, Yang Sun, Yice Zhang, Ruifeng Xu Harbin Institute of Technology

原文摘要

Argument generation is a fundamental NLP task that aims to automatically produce persuasive arguments.Effective human argumentation is inherently complex and multifaceted, integrating argumentative strategies, appropriate styles, and adaptation to target audiences, etc.However, existing studies focus on limited control signals such as topic, stance, or key aspects, failing to capture this complexity.As LLMs advance, the lack of benchmarks evaluating multifaceted argumentative control becomes a critical bottleneck.To address this, we introduce ArgGenBench, a novel benchmark containing complex instructions that integrate multi-dimensional control, including topic, stance, length, style, strategy, audience, and key points.Extensive evaluation across 15 LLMs reveals significant limitations: even the best-performing model achieves only 42.7% win rate against human-verified references.These results highlight the challenge of controlled argument generation and establish ArgGenBench as a rigorous testbed for developing more capable systems.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.93ACL Anthology · ACL 2026

When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms

Chaewan Chun, Delvin Ce Zhang, Dongwon Lee The Pennsylvania State University

原文摘要

Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and live streams. With millions of shows and hundreds of millions of listeners, audio platforms are now a major channel for misinformation. Yet existing fact-checking pipelines are mostly designed for written claims, overlooking the unique properties of spoken media. We argue that audio misinformation is not merely textual content with transcripts: it is structurally different because it is both spoken—carrying persuasive force through prosody, pacing, and emotion—and conversational—unfolding across turns, speakers, and episodes. These dual properties introduce verification difficulties that traditional methods rarely face. This position paper synthesizes evidence across modalities and platforms, examines datasets and methods, and highlights why existing pipelines fail on audio. We argue that advancing fact-checking requires rethinking verification pipelines around the spoken and conversational realities of audio.

cs.CL

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