跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+3新增论文
+3其中 AI 相关
+1已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-12 17:17 · 全站最近入库：2026-11-12 06:37各信源进度 →

统计口径与数据新鲜度

统计 2026-11-11 17:33 — 2026-11-12 17:33（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：2026-11-12 17:17最近发现新增版本：2026-11-12 06:37库内论文最新更新时间：2026-11-11 08:00

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。

21,675 已收录论文18,323 AI 相关6,056 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 6 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-12 17:15（北京时间）
新论文 · v1arXiv:2611.13802

Scaling Shapley Energy Attribution to Large LLM Batches

Qi Luo, Ziwen Wang, Kunlin Li, Changxi Liu, Yun Chen

原文摘要

LLM services have become essential infrastructure for applications such as chat assistants and coding agents. Service providers need to attribute resource consumption to individual requests for purposes such as billing. Among these resources, energy is a key metric for assessing the cost of serving each request. However, serving engines batch requests together, and GPUs report only aggregate energy consumption. Providers typically estimate each request's share in proportion to its token consumption, but this approach can produce substantially unfair estimates. The Shapley value provides a principled solution to fair energy allocation, but computing it directly requires $2^N-1$ GPU runs for $N$ requests, which is infeasible for large batches. To address this problem, we propose StepSplit. Across GPU generations, we observe that the energy consumed by each inference step, a forward pass over a batch, consists of a shared energy, largely due to reading model weights, and a request-specific energy. We derive a closed-form Shapley value: each request is charged its own energy plus an equal part of the shared energy of every step in which it participates. A microbenchmark of 2.5-17.8 minutes calibrates both parts of the step energy once per model and GPU configuration. Under static batching, StepSplit uses this closed form in a single pass over the scheduler log. Under continuous batching, removing a request can change how the remaining requests are batched, so StepSplit adds a correction to the closed form, computed by replaying the scheduler on a CPU. We evaluate StepSplit against Shapley values measured using 529,513 GPU runs. Under static batching with batch sizes of 8-256, StepSplit achieves an $L_1$ error of 0.4-4.3%, compared with 13.4-61.3% for token-proportional allocation. Under continuous batching with 8-128 requests, the corresponding errors are 0.5-6.4% and 20.6-71.7%.

cs.DC · cs.AR · cs.PF

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13762

Exploiting What NVIDIA Profilers Hide: Fast and Accurate Analytical GPU Performance Modeling

Ziwen Wang, Qi Luo, Kunlin Li, Changxi Liu, Dongsheng Wang, Yun Chen

原文摘要

GPUs have become essential to many fields, including AI and scientific computing. As GPUs grow more complex, the design space of their hardware configurations becomes increasingly large, yet cycle-level simulators remain too slow to explore it. Analytical models make this exploration much faster. However, existing GPU analytical models fall short of accurately predicting performance across hardware configurations, because they re-estimate or scale every stall of a warp on the target configuration. We observe that fixed-latency stalls stay the same across configurations, whereas queueing stalls vary with the pressure on each hardware unit. Based on this observation, we propose ForeSight, a queueing analytical model that reuses the fixed-latency stalls measured on GPU hardware and recomputes the queueing stalls of each hardware unit from its pressure. To provide ForeSight with the counter values of each hardware unit, we build UnitSight, a profiler that recovers these values, which NVIDIA's official profilers hide by aggregating across units. Our evaluation on hardware configurations of nine NVIDIA GPUs shows that ForeSight predicts kernel execution time with a mean absolute percentage error (MAPE) of 8.3%, compared with 14.1% for the best baseline. ForeSight also predicts the end-to-end execution time of LLM inference on vLLM with a MAPE of 2.0%. It runs 49x faster than the cycle-level simulator Accel-Sim and faster than interval-based models such as GCoM. With the aid of ForeSight, we explore configurations derived from the H20 GPU for LLM inference and find one with 1.78x its performance/cost.

cs.AR · cs.PF

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.00481

Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces

Junlong Liu, Haobo Wang, Weiqi Luo, Xiaojun Jia

原文摘要

Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs). While prior work has primarily studied attacks and defenses at the prompt level, we show that this prompt-centric paradigm overlooks a structural vulnerability in stateful, function-calling environments. In such applications, developer-defined schemas, structured arguments, and untrusted tool outputs are interleaved into a single shared model context. This architecture expands the attack surface by blurring the boundary between trusted control logic and untrusted data, allowing adversarial intent to be distributed across a multi-turn execution path. We exploit this architectural flaw through SMT, a black-box attack framework based on Simulated Moderation Traces. Departing from purely prompt-based interactions, SMT constructs a multi-turn trajectory that simulates a legitimate moderation-auditing workflow. Within this trajectory, a fabricated moderation frame leverages red-team testing as a pretext to elicit harmful generations. The subsequent validation feedback treats safety refusals as execution failures, prompting refinements that gradually weaken the model's safety constraints and ultimately trigger harmful outputs. Extensive empirical evaluations on prominent commercial LLMs from five different providers across two standardized safety benchmarks show that SMT consistently achieves the highest average attack success rate and HarmScore while requiring a near-minimal number of queries, substantially outperforming existing baselines. These findings demonstrate that prompt-level sanitization alone is fundamentally insufficient for defending tool-enabled LLM systems and highlight the urgent need for context-aware validation across schemas, arguments, tool outputs, and accumulated conversation state. The code is available at https://github.com/liujlong27/SMT.

cs.CR · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39853

KV Cache Quantization for Diffusion Language Models

Yan Tai, Yunan Ding, Guangzhao Li, Siqi Luo, Chaoyang Zhang, Guangtao Zhai, Guodong Guo, Xiaohong Liu

原文摘要

KV cache quantization is well studied for autoregressive language models, where cached prefix states are exact under causal attention and quantization is mainly a cache reconstruction problem. Diffusion language models (dLLMs) break this assumption. Fast dLLM inference reuses KV states across denoising steps, but bidirectional token representations can change as masked tokens are revealed, so the reused cache may already differ from full recomputation before quantization is applied. We study KV cache quantization under this cache-reuse error. We adapt KIVI and KVQuant to Fast-dLLM-style prefix and dual cache reuse, and introduce TRACE and dLLM-KVQ. TRACE reconstructs feasible dLLM reveal trajectories from prompt-answer pairs, providing calibration states that match iterative masked decoding. dLLM-KVQ uses these trajectories to choose per-layer K-cache entries that remain in high precision according to output- and attention-level behavior metrics, while applying standard low-bit value quantization. Across LLaDA and Dream evaluations on GSM8K, HumanEval, MATH, and MBPP, dLLM-KVQ achieves the highest average score among quantized methods across the reported 4-bit and 2-bit prefix/dual cache settings.

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36685

RoboSTD: Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring for Bimanual Learning

Hongyu Wu, Chong Liu, Shaoxuan Xie, Shaqi Luo, Hanyu Feng, Shihan Wu, Guocai Yao

原文摘要

Bimanual robot manipulation plays an important role in real-world robotic applications, but the high cost of collecting bimanual demonstrations limits the scalable development of bimanual policy learning. Meanwhile, existing single-arm manipulation data are difficult to directly transfer to bimanual systems due to the Single-to-Dual gap in action spaces, coordination requirements, and arm-side spatial preference. To address this issue, we propose RoboSTD, a zero-shot Single-to-Dual data construction method for bimanual robot learning. RoboSTD first applies sagittal-plane mirroring to observations, proprioception, and actions to construct executable contralateral pseudo-demonstrations. It then uses an LLM to generate structured spatio-temporal coordination constraints that guide the reconstruction of pseudo-bimanual supervision across workspace regions and task stages. We evaluate RoboSTD on the AgileX ALOHA platform in both RoboTwin simulation and real-world settings. Experiments demonstrate the effectiveness of RoboSTD for bimanual policy learning. Without requiring additional bimanual demonstrations, RoboSTD increases the average number of collected cups to 2.11x that of the single-arm baseline in the cup collection task. Project page: https://huahungy.github.io/RoboSTD/

cs.RO

查看论文
arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.232ACL Anthology · ACL 2026

Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection

Yuhang Yang, Kai Tang Zhejiang University, Chao Ye, Haobo Wang Zhejiang University, Qiqi Luo Alibaba Group, Jin Guang Zheng Alibaba Group, Zhixin Zhang

原文摘要

Debt collection is a critical negotiation task in the financial industry, with strong practical relevance and exceptional academic value as a behaviorally rich, high-stakes testbed for human-centered dialogue systems. While large language models (LLMs) have shown promise in dialogue and negotiation, effectively evaluating their performance in this complex scenarios remains a major challenge: existing benchmarks uniformly assume users to be static, rational agents with fixed preferences, failing to capture the rich behavioral heterogeneity inherent in real-world debt collection. To bridge this gap, we propose DebtBench, the first public persona-enriched debt collection benchmark, that highlights behavioral heterogeneity in negotiation. Moreover, we develop DebtGPT, a debt collection agent trained to jointly optimize financial recovery and interaction experience. Our experimental results, using 16 state-of-the-art LLMs, find that most existing models struggle in this complex but realistic scenarios, whereas DebtGPT outperforms all open-source baselines and achieves performance on par with GPT-4o. The code and data are available at https://github.com/yyuhhhh13/DebtNegotiation.

cs.CL

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