跳到正文
研踪PaperTrail

论文库

检索计算机领域论文，按研究方向、作者或关键词筛选。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+2已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-15 01:33 · 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

统计 2026-11-14 01:52 — 2026-11-15 01:52（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-15 01:33最近发现新增版本：2026-11-14 15:09库内论文最新更新时间：2026-11-13 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

23,348 已收录论文19,580 AI 相关6,729 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 17 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-15 01:32（北京时间）
版本更新 · v4arXiv:2609.10947

The Platonic brain bridge hypothesis: brain networks as architectural priors for multimodal large language models

Pengfei Zhang, Biao Tian, Xiangang Li, Li Liu

原文摘要

Multimodal large language models predict brain activity, but alignment has remained a measurement, not a design tool. We propose the Platonic brain bridge hypothesis: omni models, which jointly process video, audio and text, share brain-like representations usable in both directions. From model to brain, seven omni models show stable brain-likeness across participants, and our encoding experts lead the Algonauts 2025 out-of-distribution leaderboard by mean score. From brain to model, we make three contributions. Brain-MoE ties the experts of a frozen model to the seven cortical networks, raises held-out accuracy in all 15 model-benchmark pairs and exceeds capacity-matched random experts in 14. Brain-AVQA, to our knowledge the first audiovisual benchmark with cortical-network labels for every question, supplies the pretraining questions. Brain-Scope finds sparse features that predict brain responses, steer routing and, when removed, weaken brain prediction. Human brain organization is therefore a usable architectural prior for multimodal large language models.

q-bio.NC · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15439

DeALR: Decoupled Audio-Latent Optimization with Acoustic Backtracking for Test-Time Reasoning

Haoyu He, Yan Rong, Li Liu

原文摘要

Large audio-language models (LALMs) typically encode audio once and conduct subsequent multi-step reasoning in a language-dominated space, weakening access to fine-grained acoustic evidence. Although recent multimodal approaches have explored re-engaging perceptual inputs during reasoning, whether a frozen LALM can actively revisit crucial acoustic evidence in the latent space at test time remains underexplored. To bridge this gap, we introduce Decoupled Audio Latent Reasoning (DeALR), a novel test-time latent reasoning framework that shifts the paradigm from text-dominated deliberation to active audio-latent interaction. Inspired by the human cognitive process of "backtracking" to specific acoustic cues during complex listening tasks, DeALR establishes a dynamic interaction between learnable thought tokens and acoustic segments. Specifically, we introduce Span-Level Acoustic Backtracking, which traces initial attention distributions to dynamically isolate contiguous, key acoustic spans. Furthermore, we propose Decoupled Audio-Latent Optimization (DALO), a test-time reinforcement learning algorithm that jointly refines thought vectors and acoustic patches via confidence-guided policy gradients. Crucially, DALO explicitly decouples trajectory search from response generation by reinstating uncorrupted, high-fidelity audio embeddings prior to text synthesis, entirely eliminating optimization-induced distribution drift while retaining the navigation of the refined thoughts. Comprehensive experiments on benchmarks demonstrate that DeALR consistently outperforms prior test-time optimization approaches, delivering substantial improvements in both confidence calibration and reasoning accuracy.

cs.SD

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15237

Regret Bounds for Quantum Linear Bandits

Maoli Liu, Zhuohua Li, John C. S. Lui

原文摘要

We study $d$-dimensional quantum linear bandits with bounded rewards, where the learner selects an action and queries its quantum reward oracle or its inverse in each round. For general action sets, existing algorithms achieve regret $O(d^{3/2}\operatorname{polylog}(T))$ over $T$ rounds. Prior work established a lower bound of $Ω(d\log(1+T/d))$, leaving open whether the $d^{3/2}$ dependence in the upper bound is necessary. We resolve this question by proving a minimax lower bound of $Ω(d^{3/2}\log(1+T/d^{3/2}))$. To prove this result, we construct a family of hard instances in which the reward gap of each action is proportional to its squared distance from the optimal action. We establish a posterior Fisher information bound for adaptive quantum queries with quantum memory, relating the information acquired about the optimal direction to the regret incurred. Viewing each selected action as an estimate of the optimal direction, we use a matrix Cramér--Rao argument to derive a lower bound on expected regret. When the action set is fixed and has size $K$, we propose two phased elimination algorithms, Soft-G-Elim and QG-Elim. We adapt quantum phase estimation to construct SoftQME, a mean estimator with small bias and controlled tails. Soft-G-Elim estimates the mean reward of every surviving action from independent SoftQME outputs on the support of an experimental design. Controlling the bias and fluctuations of these estimates yields regret $O(d\log T\log(dT)\sqrt{\log(KT)})$, reducing the $\log(KT)\log\log(dT)$ factor in the previous regret bound to $\sqrt{\log(KT)}$. QG-Elim further reduces this factor to $\min\{\sqrt{\log(KT)},\sqrt d\}$.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.09117

PathXAI: Evidence-guided structured pathology reporting by transferable tissue segmentation

Zhaorui Tan, Jun Aun Tan, Weixiang Zhong, Xiaoxi Wang, Tan Pan, Xingdong Xue, Li Liu, Jing Zhang 等 10 位作者

原文摘要

Pathology reporting translates tissue observations across regions and slides into diagnoses and supporting morphological descriptions, yet explicit tissue-composition measurements are rarely used to guide H&E-based report generation. We developed PathXAI, which pairs transferable tissue segmentation with a pretrained vision-language model. Because supplying tissue information directly with H&E images gave limited benefit, PathXAI-Report uses segmentation overlays and tissue statistics in separate steps, selects between candidate diagnoses by fixed rules, acquires further images when evidence is insufficient and records every step for review, without updating model weights. Its segmentation component exceeded baselines in mean Dice on internal and external tests. In automated evaluation on 1,200 public and 151 private cases, PathXAI achieved higher agreement with reference diagnoses and documented morphology than H&E-based comparators. On the public cohort, it exceeded Qwen+H&E by 4.4 percentage points in cancer family match rate and 5.7 percentage points in finding F1.

cs.CV · cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09665

Online Causal Configuration for Networked Systems via Doubly Robust Steady-State Learning

Yuli Liu, Zhiheng Zhang

原文摘要

Measurement exploration reveals how interacting local controllers behave but can alter the steady-state regime a network operator seeks to optimize. Exploratory rewards may therefore fail to identify a configuration's no-exploration value: under full exploration, identical logged distributions can conceal different target values. Identification requires accounting for both action-law mismatch and shifts in the steady-state context distribution. We introduce CAUSAL-UCB, which selects configurations using optimistic bounds on target causal values. Its interference-aware clipped doubly robust evaluator produces predecision certificates separating statistical variation from calibrated identification, transport, and temporal-dependence errors. For any finite horizon and predetermined schedule of measurement-exploration rates $\{η_t\}$, we prove lower bounds of $Ω((1+\sum_tη_t)^{-1/2})$ on minimax inference error and $Ω(\sum_tη_t)$ on measurement-induced deployment loss in a stationary, potentially slow-mixing family. We bound regret under valid certificates and selected-information growth, and establish an anytime finite-sample certificate with a shared radius for estimation, selection, and reporting in a restricted known-local-interference model. In a high-mismatch benchmark with 24 paired seeds and 200 rounds, CAUSAL-UCB attains target regret of $1.732$ versus $6.326$ for certified IPS-UCB, with deployment gaps of $30.607$ and $23.217$, respectively. Independent-audit coverage tests and trace-driven semi-synthetic studies further assess certificate coverage and deployment trade-offs.

cs.LG · stat.ML

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.11316

MetaEncoder: Exploring the Limit of Bi-Encoders for Multimodal System One Decision Making with Natural Language Interface

Jianpeng Cheng, Guangyu Sun, Aashu Singh, Benyu Zhang, Haixing Dai, Hossein Mansour, Jiangfan Zhang, Shlok Kumar Mishra 等 14 位作者

原文摘要

System One models prioritize fast, low-latency decision-making and calibrated probability distributions over token generation. While prevailing systems rely on structured schemas and typed outputs, candidate-scoring trade-offs remain poorly understood. We systematically investigate the scaling dynamics, accuracy, and inference efficiency of three scoring architectures adapted from a pre-trained decoder: bi-encoders, cross-encoders, and constrained decoders. Experiments show clear performance scaling with model size across all three architectures. Cross-encoders consistently achieve the highest accuracy across model sizes, while bi-encoders and constrained decoders perform similarly as training tokens scale. Efficiency-wise, bi-encoders maintain near-constant inference latency as candidate pools grow because candidate embeddings can be cached offline, whereas cross-encoder and constrained-decoder costs scale quadratically with candidate size. This efficiency advantage makes bi-encoders uniquely suited to bridge small closed-set decisions and massive open-set retrieval. For closed-set tasks, including all candidate options in the request prompt is critical for bi-encoder competitiveness. Guided by these insights, we introduce MetaEncoder, a multimodal encoder fine-tuned from a Muse-Glimmer decoder via unidirectional contrastive learning to match requests and candidates. We present packed contrastive training to maximize efficiency for dynamically allocated negative candidates, enabling memory-efficient full fine-tuning of a 30B decoder into an encoder. Evaluated across 12 benchmark suites spanning closed-set decision-making, multimodal understanding, and open-set retrieval, MetaEncoder outperforms state-of-the-art multimodal embedding baselines on decision tasks and MMEB-v3 image/video retrieval, while scaling to open candidate spaces far too large for current decision models.

cs.CL · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08879

GeoJeV: Unified Spatial Probability for Training-Free Efficient Evidence Acquisition in Ultra-High-Resolution Remote Sensing

Xinyi Niu, Lingfeng Chen, Jiehua Zhang, Panhe Hu, Li Liu, Zhen Liu, Yongxiang Liu

原文摘要

Ultra-high-resolution (UHR) remote sensing imagery provides rich spatial details but poses challenges for accurate and efficient multimodal understanding. Existing evidence acquisition methods selectively explore relevant image regions, yet largely rely on generative multimodal large language models (MLLMs) for intermediate search control. However, reinforcement learning from human feedback (RLHF) primarily aligns these models with human-preferred responses rather than explicitly optimizing region-level relevance judgments, while repeated autoregressive decoding introduces additional computational overhead. To address these limitations, we propose GeoJeV, a training-free method that formulates evidence acquisition as spatial probability-driven decision making. At each evaluated region, a frozen JeV model produces a probability distribution over candidate regions. Specifically, Region Rank prioritizes candidate regions, Sparse Branch adaptively determines branching width through sparse probability support, and Depth Gate controls refinement depth based on branch sparsity and distribution concentration. By coordinating search priority, branching width, and refinement depth through a shared distribution, GeoJeV reduces reliance on RLHF-aligned generation and eliminates intermediate decoding. Experiments on LRS-GRO, MME-RealWorld-RS, and LRS-VQA achieve accuracies of 60.59%, 52.79%, and 34.32%, respectively. Across the three benchmarks, GeoJeV improves the average accuracy of Qwen3-VL-8B by +5.32pp with only 1.12 additional seconds per question, while achieving a 7.8-fold inference speedup over the recent method.

cs.CV · eess.SP

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39287HF 社区推荐

OpenFlyV2: An onboard vision-language-action model for aerial perception and navigation

Junli Liu, Zhongrui You, Chenhui Li, Zhen Li, Yuyang Tian, Qizhen Chen, Delin Qu, Haoming Song 等 12 位作者

原文摘要

Human cognition flexibly draws on a shared perceptual foundation to understand environments, attend to referenced entities, reason about spatial relationships, and act. Endowing aerial robots with an analogous functional organization remains challenging, as existing systems are often specialized for individual capabilities, dependent on offboard computation, or unable to sustain closed-loop operation on resource-constrained platforms. We present OpenFlyV2, a unified onboard vision-language-action architecture that uses a shared vision-language representation to support semantic understanding, language-grounded perception, spatial reasoning, and continuous aerial control within a single model. A compact action expert converts the shared multimodal context into short-horizon flight waypoints, while motion-aware temporal memory and action priors reduce the computational cost of closed-loop execution. We construct \dataset{}, a simulation-based dataset comprising approximately 70,000 navigation trajectories and 60,000 vision-language instruction--response pairs. OpenFlyV2 generates action chunks, each comprising 12 waypoints, entirely onboard at 10 Hz using an NVIDIA Jetson Orin NX. It transfers zero-shot from simulation to physical quadrotor flight, achieving a 48\% navigation success rate across four real-world environments without real-world aerial training data. Extensive results demonstrate that broad vision-language competence and continuous closed-loop flight can coexist within a single model on a resource-constrained aerial platform. Code will be made publicly available at https://github.com/Ideal-ljl/AeroBrain.

cs.RO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39125

Tracking Task Progress Through Errors and Recovery:A Benchmark and Recurrent Distributional Model

Chen Wang, Xinze Niu, Zihao Zhu, Keru Zhou, Yicheng Xu, Shihao Jin, Wenhao Wang, Bowen Jiang 等 11 位作者

原文摘要

Operational strategies for long-horizon robotic tasks require reliable evaluation to determine whether execution is progressing, stalling, regressing, or recovering from errors. Yet real- world robot datasets that capture error recovery and provide frame-level error annotations remain scarce. To address this gap, we introduce and open-source ReSTEP, a dataset of 3,156 episodes across two manipulation tasks with semantic stage labels and frame-level er- ror annotations. ReSTEP supports training and benchmarking progress monitors through successful execution, mistakes, and recovery. Existing progress estimators can struggle to distinguish visually similar task stages and maintain temporally consistent predictions while responding to genuine setbacks. We therefore propose REMORA, an online progress esti- mator that combines recurrent visual memory, distributional prediction, and causal peak selection. Memory retains execution history to help resolve visual ambiguity, while distribu- tional prediction represents alternative progress hypotheses and causal peak selection tracks a supported hypothesis over time. Together, these components enable consistent progress tracking that remains responsive to errors and recovery. Experiments on both ReSTEP tasks demonstrate that REMORA outperforms the state-of-the-art SARM baseline in progress ac- curacy and temporal consistency.

cs.RO

查看论文
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

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38398

Agent Transfer Protocol: Server-Mediated Agent-to-Agent Communication Architecture for the Agentic Web

Yuqi Qiu, Zuyao Xu, Lu Sun, Xiang Li, Fasheng Miao, Fubin Wu, Haixin Duan, Zheli Liu

原文摘要

The Agentic Web needs to support continuous collaboration among agents from different organizations. These collaborative relationships may span multiple tasks, network connections and execution instances. Such interactions require stable communication entries, message reception when instances are offline, and source verification after delayed reading. How to organize these responsibilities so that cross domain communication does not depend on specific execution instances being continuously online is a key issue for building long term agent interaction infrastructure. We propose ATP (Agent Transfer Protocol), a cross organization agent communication protocol adopting a domain mediated architecture. In this architecture, administrative domains maintain stable addresses and receiving entries for logical agents. The domain servers of both parties continuously undertake cross domain transmission. The receiving domain verifies and saves messages for execution instances to read later. The sending domain signs the complete message envelope. This allows the receiving instance to independently reverify the domain source and content integrity after the original connection ends, provided the original verification public key is still trusted and obtainable. We implemented an ATP prototype and evaluated it through a controlled cross organization LLM agent workflow, mechanism checks and latency measurements. The workflow completed subsequent business processing after instances went offline or were replaced. The independent verifier successfully verified the domain source and content integrity of saved messages. Latency measurements further quantified the communication and processing overhead introduced by the continuous mediation of domain servers.

cs.AI · cs.MA

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38041

CARF: Context-Aware Reasoning Supervision for Scientific Evidence Screening

Nishtha Agarwal, Adam Visokay, Astha Wahi, Kwabena Owusu Aninkora, Amy Gu, Tyler McCormick, Li Liu

原文摘要

Systematic reviews synthesize biomedical evidence to inform clinical and public-health decision-making, but require domain experts to manually screen thousands of studies against detailed eligibility criteria. We investigate whether language models can assist with this bottleneck by learning from expert feedback on title and abstract screening decisions and model-generated reasoning. We introduce the Context-Aware Reasoning Framework (CARF), a human-in-the-loop framework that uses expert feedback on model-generated reasoning to adapt language models to context-dependent screening tasks. We evaluate CARF on systematic reviews of verbal autopsy and cause-of-death ascertainment methods used in global public health mortality surveillance. CARF shows competitive screening performance compared to evaluated models while retaining high sensitivity, and is more inclined to retain ambiguous, context-dependent studies for human review. Together, these findings suggest that expert-corrected reasoning is a promising supervision signal for context-dependent scientific screening, while retaining a role for human oversight.

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37091

GAWM: A Compact Latent World-Action Model for Multi-Task Robot Manipulation

Yang Liu, Xiaoli Liu, Liuzhao Li, Yuduo Song, Muyuan Liu, Xiang Gao

原文摘要

World-action models (WAMs) incorporate the prediction of future observations into robot policy learning. However, most WAMs build on billion-parameter vision-language or video-generation backbones, which incur high training cost and inference latency. In this letter, we present GAWM (Geometry-Aware World Model), a compact multi-task WAM that predicts future observations in the latent space of a frozen DINOv3 encoder. GAWM compresses the features of each camera view into a small set of tokens. A lightweight predictor, conditioned on the task rather than on actions, forecasts these tokens at two future horizons. The forecast is supervised with the dense patch features of future frames, which requires it to encode the spatial layout of the future scene. An action decoder directly regresses a chunk of future actions from the current and predicted tokens. With fewer than 100M parameters and no robot or video pretraining, GAWM outperforms billion-parameter WAMs on RoboTwin 2.0 and performs comparably to billion-parameter VLAs on LIBERO. On a dual-arm robot, it achieves lower action-prediction error on held-out demonstrations than a fine-tuned billion-parameter VLA, with substantially lower inference latency. Ablation studies show that the latent world model substantially improves performance when training data or training time is limited. Code is available at https://github.com/gaoxiang12/ga-wm.

cs.RO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35499

Beyond "[at]": A Large-Scale Measurement of Email Address Presentation on Computer Science Scholars' Homepages

Aichun Huang, Xiang Li, Zheli Liu

原文摘要

Publishing an email address makes a scholar reachable, but also exposes it to automated collection. Yet how email addresses are presented on the Web has not been measured systematically across their content, form, and location. We measure it across 32,413 homepage records of computer-science scholars listed in CSRankings with MailScope, which preserves raw and rendered HTML, extracts addresses with a three-stage cascade (rules, then large language models, then a crawling agent), and labels each address. Among 19,517 reachable homepages, we find an address for 17,712 scholars (90.8%). Our measurement yields four main findings. (i) Presentation is diverse: only 53.8% publish plain content, the others spread over 23 subtypes of symbol substitution, code, and semantic description, and 5.5% of scholars' addresses appear only after rendering. (ii) Presentation follows hosts rather than individuals: scholars on one host share a content class in 67.9% of pairs, compared with 38.8% across hosts of one institution, whereas research output and institution rank are barely related to presentation. (iii) Content and form are coupled through code-based links, and 6.2% of scholars with non-plain selected presentations have plain-text copies of the same address elsewhere. (iv) Plain addresses are more common behind passed bot challenges, and 36.1% of redirecting homepages no longer lead to scholars. Human assessment yields 94.4% weighted exact-set agreement on 143 judgeable pages, complemented by three 500-page review rounds with human verification. Presentation is diverse but clusters by shared host, and by our label-based estimate plain-string matching misses nearly half of the addresses.

cs.IR

查看论文
arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1180ACL Anthology · ACL 2026

Re^3: Relevance & Recency Retrieval for Mitigating Temporal Hallucination

Jiawei Cao, Jie Ouyang University of Science and Technology of China, Mingyue Cheng University of Science and Technology of China, Zhaomeng Zhou, Chunli Liu Hefei University of Technology, Yupeng Li University of Science and Technology of China, Zirui Liu, Shijin Wang State Key Laboratory of Cognitive Intelligence

原文摘要

Retrieval-Augmented Generation (RAG) is a mainstream approach to mitigating hallucinations in Large Language Models (LLMs), yet in dynamic real-world scenarios, such as weather forecasting or evolving news events, existing retrievers suffer from both temporal-semantic misalignment and outdated-document interference. To address this, we propose Relevance Recency Retrieval (Re^3), a novel framework that mitigates temporal hallucinations via two core components: a Time-Aware Dual Relevance Encoder that embeds heterogeneous temporal signals into the semantic space to ensure retrieval fidelity, and a Conflict-Aware Recency Filter that performs listwise arbitration to identify and suppress obsolete factual versions. To rigorously evaluate this setting, we introduce Re^2 Bench, a large-scale benchmark comprising over 1.3 million instances designed to assess system robustness in realistic environments where temporal constraints and conflicting factual versions coexist. Experiments on three public benchmarks and Re^2 Bench demonstrate that Re^3 consistently outperforms the strongest baselines by an average of 9.7% in generation accuracy, with gains of up to 25.2% on challenging dynamic tasks, while demonstrating robustness across diverse RAG settings.

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.2117ACL Anthology · ACL 2026

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models

Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng International Digital Economy Academy, International Digital Economy Academy, Yingce Xia Zhongguancun Academy, Shufang Xie Renmin University of China, Kaili Liu 等 18 位作者

原文摘要

Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabilities required in practice. Most benchmarks are manually curated or domain-generic, limiting scalability and alignment with real scientific use cases. In this paper, we propose a new framework named SciCustom to address the problem. It enables the custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs. SciCustom first organizes scientific knowledge into ontology-grounded knowledge units with controlled granularity and trains a tagger to map large-scale data instances into this knowledge space. Given a custom requirement, relevant knowledge units are identified via voting-based multi-model consensus. These units enable relevance-aware benchmark retrieval via binary search, followed by proxy subset selection and data-grounded benchmark generation for efficient evaluation. Experiments in chemistry and healthcare demonstrate that SciCustom reveals fine-grained differences in LLM scientific capabilities that standard benchmarks overlook, while requiring neither expert annotation nor synthetic question generation. This work provides a scalable and application-aware foundation for benchmarking scientific capabilities in LLMs.

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.455ACL Anthology · ACL 2026

CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning

Biao Yi, Tiansheng Huang, Baolei Zhang Nankai University, Tong Li Nankai University, Lihai Nie, Zheli Liu Nankai University, Li Shen Sun Yat-Sen University

原文摘要

Fine-tuning-as-a-service, while commercially successful for Large Language Model (LLM) providers, exposes models to harmful fine-tuning attacks. As a widely explored defense paradigm against such attacks, unlearning attempts to remove malicious knowledge from LLMs, thereby essentially preventing them from being used to perform malicious tasks. However, we highlight a critical flaw: the inherent general adaptability of LLMs allows them to easily bypass selective unlearning by rapidly relearning or repurposing their general capabilities for harmful tasks. To address this fundamental limitation, we propose a paradigm shift: instead of selective removal, we advocate for inducing model collapse, effectively forcing the model to ”unlearn everything”, specifically in response to updates characteristic of malicious adaptation. This collapse directly neutralizes the very general capabilities that attackers exploit, tackling the core issue unaddressed by selective unlearning. We introduce the Collapse Trap (CTRAP) as a practical mechanism to implement this concept conditionally. Embedded during alignment, CTRAP pre-configures the model’s reaction to subsequent fine-tuning dynamics. If updates during fine-tuning constitute a persistent attempt to reverse safety alignment, the pre-configured trap triggers a progressive degradation of the model’s core language modeling abilities, ultimately rendering it inert and useless for the attacker. Crucially, this collapse mechanism remains dormant during benign fine-tuning, ensuring the model’s utility and general capabilities are preserved.

cs.CL

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