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版本更新 · v2arXiv:2604.14689

ISAC with Backscattering RFID Tags: Beamforming and Codebook Design

Hao Luo, Umut Demirhan, Ahmed Alkhateeb

原文摘要

This paper explores an integrated sensing and communication (ISAC) system with backscattering radio frequency identification (RFID) tags. In this setup, an access point employs communication beams to serve communication users while leveraging a sensing beam to interrogate RFID tags. Under the total transmit power constraint of the system, our objective is to design a joint sensing and communication beamforming codebook by considering the tag interrogation and communication requirements. To lay a foundation for the codebook design problem, we first study the beamforming design problem in the single-tag scenario and investigate two approaches: a two-stage zero-forcing-based design with optimized power allocation and a joint beamforming design that minimizes the total transmit power. Extending our study to the multi-tag scenario, we propose a site-specific beamforming codebook design. The proposed approach partitions the region of interest into angular sectors and jointly optimizes the sensing and communication beams for each sector. The simulation results show that the proposed beamforming designs can effectively mitigate the mutual interference between sensing and communication functionalities, thus enhancing the interrogation range of the tags while minimizing transmit power. Also, the efficacy of the proposed site-specific codebook design has been demonstrated in terms of interrogation success rate, offering a promising approach for ISAC backscattering systems.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13741

Retargetable Compilation for Tensor Kernels via a Unified Layout Abstraction

Likai Ma, Renze Chen, Tianqi Li, Yuhao Luo, Youwei Xiao, Size Zheng, Xiuhong Li, Yun Liang

原文摘要

Modern hardware accelerators achieve high performance through specialized instructions coupled with tightly managed memory hierarchies. These instructions impose strict layout requirements on their operands: where tensor fragments reside, how they are organized, and how parallel units access them. Such instruction-visible layout is critical to kernel performance, as it determines whether instructions execute correctly and how much data movement they incur. However, existing compilers either scatter layout requirements across handwritten programs and local scheduling rules, or provide layout representations and conversions specific to a single class of targets. Both approaches expose the same abstraction gap: the absence of a unified model for instruction layout requirements across backends. This paper presents Arknife, a retargetable compiler that treats instruction-visible layout as first-class instruction semantics. Its central idea is a constraint-based layout algebra: tensor layouts map logical axes to hardware axes spanning parallel levels and memory spaces. Instruction operands declare layout requirements, and target-provided repairs resolve conflicts. Layouts are then solved globally across the program. ISA-specific layout facts are supplied by target-side instruction descriptions rather than hardcoded in the compiler. Retargeting reuses the layout algebra and solver while defining the new target's instructions and their operand requirements. We evaluate Arknife across x86 CPU, Hexagon NPU, and NVIDIA A100 GPU. Arknife matches cuBLAS on A100, and on x86 and Hexagon outperforms vendor-optimized libraries by $2.17\times$ and $3.78\times$ and the retargetable compiler MISAAL by $5.08\times$ and $4.53\times$, respectively.

cs.PL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.35581

Integrate the Reward, Don't Sample It: Window Likelihood for Interruptions and Backchannels in Full-Duplex Speech Models

Guoqiang Hu, Lichao Zhang, Yizhou Peng, Yuhao Luo, Haifeng Hu, Zhongqian Sun, Wei Yang, Eng Siong Chng

原文摘要

When a user talks over a full-duplex dialogue model, it must decide whether the user is interrupting (stop) or backchannelling (carry on). Post-training can improve this discrimination or change how readily it stops. In our GRPO runs the second dominates: no configuration lowers both error rates on more than one model, and two released checkpoints discriminate no better than their bases. We trace this partly to how the reward is estimated. The probability of stopping inside a short window is a known function of a few stop probabilities the policy emits, yet GRPO estimates it by sampling. A group of rollouts that all agree gives no gradient, even when all fail; the push on the stop threshold is strongest where the model is unsure, whichever outcome is right; and whole-rollout credit reaches decisions the reward never judges. Confining the credit to the window removes only that leakage. Window likelihood (WL) samples the words but integrates over the stop decisions, so the probability of the correct outcome is computed exactly and its logarithm maximised. On HumDial-FDBench, across four open models, WL gives the best F1 and lowers both error rates by more than their confidence intervals, without talking into silence.

eess.AS · cs.CL · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11516

Breaking survivorship bias in driving world models

Zihao Sheng, Zilin Huang, Yuhao Luo, Zhengyang Wan, Boyue Wang, Soyoung Ahn, David A. Noyce, Sikai Chen

原文摘要

World models allow autonomous vehicles to imagine the consequences of candidate actions before execution, but their reliability depends on the futures represented in their training data. We identify a survivorship bias in driving world models: because the futures recorded in prevailing driving datasets almost always end safely, existing models often imagine benign outcomes even when a collision is imminent. We introduce a neutral world model (NWM) that learns both routine and safety-critical futures from a mixture of routine driving and real accident videos with a bounding-box-guided objective. The NWM more faithfully anticipates imminent collisions, while maintaining a low false collision rate and competitive generation quality in routine driving. We further use the model as an imagination engine for decision-making. Given multiple ego trajectories, the NWM predicts the future associated with each candidate, and a reward function evaluates these imagined outcomes before selecting an action. On challenging NAVSIM scenarios, this procedure selects the better trajectory more reliably than the baseline world model. Zero-shot experiments with real-world pedestrian dummy crossing data collected using an autonomous research vehicle further demonstrate transfer to unseen sensor observations. These results show that correcting survivorship bias enables world models to more faithfully reason about futures and support safety-critical decision-making in autonomous driving.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10683

TransMem: Transition-Aware Retrieval for Evolving Personal Memory

Shigeng Chen, Linhao Luo, Changlong Shi, Zhangchi Qiu, Shirui Pan

原文摘要

Long-term personalized agents are increasingly expected to answer queries over user histories in which preferences, goals, and constraints evolve across interleaved conversations. Existing retrieval-based and structured-memory systems can surface relevant facts, but often return isolated memory units that do not explain which earlier preference was refined, contradicted, or superseded by later evidence. We introduce TransMem, a transition-aware memory retrieval framework that stores preference updates as explicit retrieval units alongside ordinary factual memories. During memory construction, TransMem routes user interaction history into topic-structured memory graphs, detects shift events, and grounds them as transition records. At inference time, TransMem first retrieves factual anchors and then adaptively expands over transition records, producing a compact context of factual anchors and accepted transition records. Experiments on PersonaMem and PersonaBench across two Qwen backbones show that TransMem outperforms representative retrieval-based and structured-memory baselines. Controlled analyses further show that transition records and adaptive expansion contribute beyond factual anchoring, chronological expansion, flat transition retrieval, and fixed-hop traversal. These results suggest that preference-update records are useful retrieval units for long-term personalization.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10657

MMGraph-Agent: Agentic Multimodal RAG via Cache-Inspired Multimodal Knowledge HyperGraphs

Haoran Luo, Ziyue Zhu, Shangyang Wu, Linhao Luo, Yikai Guo, Qika Lin, Fangzhi Xu, Jiapu Wang 等 11 位作者

原文摘要

Driven by increasingly complex real-world applications, Retrieval-Augmented Generation (RAG) has evolved from textual pipelines to multimodal settings, comprising two complementary dimensions: knowledge organization and retrieval enhancement. However, existing methods still face challenges of high construction cost, context window limitations, and the lack of unified training across offline and online knowledge sources. To address these challenges, we propose MMGraph-Agent, a unified multimodal RAG framework based on cache-inspired multimodal knowledge hypergraphs. MMGraph-Agent enables extremely lightweight hypergraph construction with zero API cost and dynamic memory-based retrieval that alleviates long-context issues. Experiments across offline, online, and joint retrieval settings demonstrate consistent improvements in performance, efficiency, and architectural unification. Our software and data are publicly available at https://github.com/LHRLAB/MMGraph-Agent.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.25342

MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models

Linhao Luo, Thuy-Trang Vu, Van-Anh Nguyen, Junae Kim, Gholamreza Haffari, Dinh Phung

原文摘要

Aligning large language models (LLMs) with diverse and multifaceted user preferences is a fundamental challenge in personalized AI systems. Existing multi-objective alignment methods either rely on costly training or require pre-trained reward models for each preference, making it difficult for them to adapt to evolving preferences. Prompt-based personalization offers a training-free alternative, but prompting alone often provides limited steerability, as LLMs may overemphasize or overlook certain preferences and fail to give users reliable control over the relative importance of different objectives when conflicts arise, leading to suboptimal alignment. In this paper, we introduce MATO, a training-free framework for Multi-objective personalized Alignment with Test-time Optimization. MATO formulates personalization as a test-time optimization problem that steers the relative importance of multiple objectives through controllable weights during decoding, without modifying model parameters or requiring external reward models. Specifically, a reward discovery module constructs prompt-derived surrogate rewards from the backbone LLM for diverse objectives specified in natural language, while a weight optimization module dynamically adjusts objective weights based on the user's initial preferences and the partially generated response to balance competing objectives during generation. The resulting surrogate rewards and weights jointly guide an online optimization procedure over the token distribution, enabling better alignment with the target objectives. Extensive experiments across multiple datasets and backbone LLMs show that MATO consistently outperforms strong baselines, achieving Pareto-improving multi-objective alignment and stronger steerability. These results highlight test-time optimization as a promising direction for scalable, controllable, and model-agnostic personalized alignment.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.01946

Closed-Form 6-DoF Pose Estimation of Medical Tools via Gradiometer-Based Electromagnetic Localization System

Zhiwei Wu, Jiahao Luo, Yubo Pu, Siyi Wei, Yuankai Chen, Jinhui Zhang

原文摘要

Electromagnetic tracking \added[]{is promising for} remote navigation of endoluminal \added[]{medical tools}. However, existing six-degree-of-freedom (6-DoF) pose recovery approaches often require pre-calibrated workspace field maps or iterative nonlinear optimization. This letter presents a Gradiometer-Based Electromagnetic Localization System (GELS), a closed-form tracking framework that uses a compact magnetometer array as an embedded quasi-gradiometer to estimate local magnetic fields and gradient tensors. These quantities are mapped by the Euler homogeneous relation to displacements between source and array, from which Procrustes registration over multiple sources recovers the array orientation and position using at least three noncollinear sources. The algorithm requires known source positions and array geometry, but no pre-calibrated workspace field maps, initial pose guesses, or calibrated source magnetic moments. The recovered \added[]{array} pose also enables a demonstration of sublevel dipole localization by serving as a mobile magnetic reference frame. Benchtop experiments across sensor array configurations and excitation modes validate the proposed closed-form 6-DoF localization pipeline, with a median solver runtime of \SI{172.00}{\micro\second}. A perturbation analysis identifies inter-sensor inconsistency and dipole model mismatch as dominant accuracy limits, informing future sensor array and source design to improve pose accuracy.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.14171

CSI-JEPA: Towards Foundation Representations for Ubiquitous Wi-Fi Sensing with Minimal Supervision

Xuanhao Luo, Zhizhen Li, Yuchen Liu

原文摘要

Channel state information (CSI) provides a widely available sensing modality for human and environment perception, but existing CSI sensing models usually rely on task-specific supervised training and require substantial labeled data for each task, device, user, or environment. This limits their scalability in practical deployments where unlabeled CSI is abundant but labeled data is costly to collect. In this paper, we present CSI-JEPA, a self-supervised predictive representation learning framework for label-efficient and transferable Wi-Fi sensing. CSI-JEPA learns reusable temporal-spectral representations from unlabeled CSI samples by predicting latent features of masked channel regions from visible context. To better match the physical structure of CSI, it jointly tokenizes CSI along the time and subcarrier dimensions and introduces a channel variation-aware masking strategy that prioritizes regions with stronger local channel dynamics. After pretraining, the encoder is frozen and adapted to downstream sensing tasks using lightweight task-specific adapters. We evaluate CSI-JEPA on seven real-world Wi-Fi sensing tasks spanning diverse objectives and deployment settings. The results show that CSI-JEPA improves downstream sensing performance over competitive baselines, achieving up to 10.64 percentage points (pp) mean accuracy gain over the supervised Transformer and matched-budget label savings of up to 95.0%. Across unseen tasks, environments, hardware platforms, and CSI organizations, frozen CSI-JEPA improves downstream adaptation by up to 22.92 pp over non-pretrained baselines, demonstrating strong representation transferability.

cs.LG · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06471

A 3D foundation model unifying CT interpretation and segmentation

Yulu Xie, Zilong Li, Shen Xu, Yifan Bai, Liuhong Shi, Jiao Chen, Xianglei Kong, Wenchao Zhu 等 17 位作者

原文摘要

Radiologists interpret computed tomography (CT) scans by jointly determining what a finding represents and where it lies in three-dimensional (3D) anatomy. However, CT models have treated CT interpretation and 3D segmentation as separate tasks, leaving textual findings spatially unverifiable and overlooking potential synergy between clinical understanding and spatial perception. Here we present HoloCT, a generalist 3D foundation model unifying CT interpretation and voxel-level segmentation by jointly modelling whole-volume context and fine-grained anatomical detail, providing 3D masks as evidence. We curated HoloDATA, comprising 195,921 CT scans from 73 public datasets and six clinical cohorts, with 6.2 million instruction dialogues and 2.8 million CT-mask pairs. Across 346 tasks spanning segmentation, lesion screening, visual question answering, and report generation on 61 internal and 12 external datasets, HoloCT outperformed specialist and foundation models. In multicentre validation across 11,455 clinical CT scans from five hospitals, HoloCT outperformed competing models in lesion segmentation and report generation by 18.2 and 15.3 points. It also achieved 85.1% mean accuracy in screening thyroid tumours and colon lesions, exceeding all four participating radiologists in a retrospective reader study. Further analyses revealed bidirectional semantic-spatial synergy: language-guided segmentation delineated unseen anatomical structures and lesions as well as diffuse free-text findings, outperforming the leading approach by 22.1 points, whereas prompting the model to generate corresponding 3D masks alongside its textual outputs significantly improved interpretation performance. HoloCT shows that clinical understanding and spatial perception can be learned together within a single foundation model and offers a path towards more accurate and reliable CT analysis.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.13245

Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation

Joonkyung Kim, Yanze Zhang, Wenhao Luo, Yiwei Lyu

原文摘要

Safe decentralized navigation in heterogeneous robot teams is challenging because robots with different dynamics and control limits cannot satisfy identical collision-avoidance obligations equally well. Existing methods often rely on model-specific safety formulations or allocate avoidance responsibility without considering whether the assigned control is physically realizable, possibly leading to safety-constrained QP infeasibility and safety violations. We propose Capability-Aware Heterogeneous Control Barrier Functions (CA-HCBF), a decentralized framework for consistent safety enforcement and capability-aware responsibility allocation. We derive a unified operational-space second-order control-affine formulation for holonomic and nonholonomic mobile robots through canonical transformation and backstepping, addressing the relative-degree mismatch while preserving forward invariance of the pairwise safe set. Second, we quantify each robot's achievable control authority along the safety-critical direction using a support-function-based capability metric and use it to distribute pairwise avoidance responsibility. A feasibility-aware clipping mechanism further mitigates physically unrealizable responsibility assignments. Simulations with up to 30 heterogeneous robots and hardware experiments demonstrate high task completion with low safety violations and reduction in local QP infeasibility.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40490

超高速率量子LDPC码的快速准确神经分诊解码

Fast and accurate neural triage decoding of ultra-high-rate quantum LDPC codes

Hong-Ye Hu, Zhenlai Wang, Binzhao Luo, Tao Xiang, Chen Zhao

AI 速读 基于摘要

提出神经分诊解码框架，用综合征一致性自适应分配神经计算，在高码率qLDPC码上兼顾近最优精度与大幅降低的解码延迟。

quant-ph · cs.AI · cs.IT · cs.LG

arXiv ↗PDF ↗
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Quantum error correction is essential for fault-tolerant quantum computing. High-rate quantum low-density parity-check (qLDPC) codes have emerged as a promising approach to reducing the substantial physical qubit overhead of quantum error correction, enabling encoding rates exceeding $1/2$ in blocks of approximately one thousand physical data qubits. However, their practical implementation requires decoders that combine high accuracy with low latency to keep pace with quantum hardware. Existing decoders of high-rate qLDPC codes can achieve high accuracy but often incur substantial computational latency, whereas neural network decoders have yet to be demonstrated for high-rate qLDPC codes. Here, we introduce \emph{neural triage decoding}, a framework that uses syndrome consistency to adaptively allocate neural computation. A local neural message-passing network rapidly predicts error corrections, additional iterations reduce residual syndrome violations, and a rarely invoked diversification stage searches for consistent corrections to the remaining shots. We demonstrate that the \texttt{Triage} decoder can decode ultra-high-rate qLDPC codes encoding a few hundred logical qubits, including lifted-product and Kasai codes, achieving state-of-the-art accuracy comparable to the near optimal algorithmic decoders while reducing decoding latency by approximately one to two orders of magnitude. Our framework is inspired by a rigorous analysis of the exact bitwise marginal decoder, revealing a distance-independent syndrome-inconsistent error floor. In addition, we show the robustness of neural network decoders to mismatched error priors. Our results establish adaptive neural decoding as a promising route towards fast, accurate, and scalable quantum error correction in the near term.

新论文 · v1arXiv:2610.39585

TactoHand: Learning Geometry-Based Dense Hand-Surface Interaction Fields from Video

Ziheng Xi, Weibo Li, Jiayi Yu, Sipeng Zheng, Wanpeng Zhang, Haiweng Xu, Hao Luo, Chuan Mao 等 9 位作者

原文摘要

Understanding how humans interact with the world requires a representation of physical interactions at the hand surface. Tactile sensing captures these interactions, but collecting tactile data at scale requires specialized hardware. We introduce TactoHand, a model that predicts three dense hand-surface interaction fields---contact, proximity, and 3D force---from monocular video. We derive geometry-based contact and proximity labels and motion-derived force labels from eligible training data within a curated collection spanning approximately 50 million source frames. TactoHand uses a temporal transformer to aggregate visual evidence across frames, while three learned queries decode the corresponding hand-surface fields. A differentiable physics layer further constrains force predictions using observed object motion. Across in-domain and out-of-domain benchmarks, TactoHand improves contact and proximity estimation over prior methods. In addition, physics supervision reduces position and rotation errors in motion rollouts, and the predicted contact and proximity fields improve downstream hand-object reconstruction. Together, these results show a scalable, low-cost path to learning hand-surface interaction fields without collecting tactile measurements. Code will be released at https://github.com/zhenqis123/Tactohand.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37188

Negative Results for Reciprocity-Based Over-the-Air Split Learning Under Channel Aging: Stability, Alignment, and Geometry

Tianhao Guo, Xianzhong Li, Hao Luo

原文摘要

In reciprocity-based over-the-air split learning, channel aging rotates and attenuates the derivative signal returned to the transmitter. Seven case studies combine numerical forecasts, mechanism checks, and competing predictions. Near the correlation zero, an archived SGD comparison finds truncated inversion 0.000813 lower in cross-entropy than polar correction, with a paired 95% interval for the difference from -0.001853 to 0.000060, and a retained-mode quadratic proxy slightly favors truncation. Selecting the return port with the largest received power falls short of the predicted five-point alignment advantage over random selection at both tested spacings, gaining at most 0.97 points, while the local inverse relation between amplitude and phase gradient is supported. Seven specified interference-induced accuracy-collapse predictions are rejected, and independent zero-mean interference preserves the conditional mean under a fixed linear chain. Per-subcarrier correction and spherical-wavefront compensation do not deliver their predicted accuracy gains. Focusing leaves both methods near chance in a single-mode model and adds no observed gain in a model with a line-of-sight component plus scattering. A successful port assignment under nonsymmetric motion refutes an exact-symmetry requirement. The competing positioning-error prediction is supported. Gaussian rail jitter with a standard deviation of 0.383 wavelengths yields high terminal test accuracy, whereas a fixed bias of the same magnitude yields near-chance accuracy. These cases distinguish valid local physical relations from unsupported training forecasts through task support, noise injection, and update moments.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37174

Deep Holes and Aperture: Sign Laws for the Empirical Correlation of Lattice-Sampled Random Wave Fields

Tianhao Guo, Hao Luo, Xianzhong Li, Yuzhi Yang

原文摘要

A Gaussian random wave field is sampled on a lattice within a bounded aperture. After displacement, re-indexing leaves a residual in the Voronoi cell. We study whether the empirical lag correlation has positive real part throughout the cell. For each realization in a single-output reciprocal link with noiseless over-the-air return, this is equivalent to the negative returned gradient being a descent direction for all residuals and differentiable losses with nonzero error signals. Return noise is treated exactly. For isotropic fields, ball-shaped apertures, unshifted lattice points, and dilations approaching the critical covering radius, every minimizer is a deep hole except with exponentially small probability. A Gaussian vector on the deep holes determines the limiting failure probability, with explicit covariance and lattice-independent limiting normalization. Square, hexagonal, simple cubic, and body-centered cubic lattices have two, three, four, and twelve sign classes. At fixed target failure probabilities below the critical limiting value, this covariance determines the exact leading aperture constant. When the margin and target failure probability vanish jointly, that constant is common to lattice shapes within each dimension. A cubic correction yields vanishing relative tail error in a moderate-deviation regime. A non-asymptotic two-sided aperture law has explicit constants. With a Rayleigh-faded specular path and positive diffuse margin, a failure floor occurs at every positive path power exactly when the cosine of its phase change is negative somewhere in the cell. The floor vanishes for all directions exactly when the covering radius is at most a quarter wavelength.

cs.IT · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34866

UniGFM-RAG: Unify Graph Foundation Models and Large Language Models for End-to-End Retrieval and Reasoning

Zicheng Zhao, Linhao Luo, Zongji Yu, Junnan Dong, Yanchao Tan, Shirui Pan, Chen Gong

原文摘要

Large Language Models (LLMs) have shown remarkable capabilities across tasks, but they still struggle with complex reasoning that requires integrating scattered knowledge. Graph Retrieval-Augmented Generation (GraphRAG) addresses this challenge by organizing fragment knowledge as graphs, while recent Graph Foundation Models (GFMs) further improve GraphRAG with graph-native retrieval and reasoning capabilities. However, existing GraphRAG methods usually adopt decoupled retrieve-then-generate pipelines, which create objective misalignment between retrieval and reasoning, and training GFM retrievers typically depends on expensive human annotations. To address these limitations, we propose \ourmethod, a unified end-to-end GraphRAG framework that tightly couples a GFM retriever with an LLM generator. Given a query, the GFM retriever first reasons over the graph to score and retrieve relevant document nodes. These node embeddings are then passed through a fully differentiable graph bridge, which projects them into soft-weighted graph prefixes according to retrieval scores and combines them with raw document texts to form graph-augmented prompts. In this way, structural information is injected directly into LLM generation, while the final answer loss can be back-propagated through the bridge to jointly optimize retrieval and reasoning. To stabilize training and reduce reliance on costly annotations, we further introduce a semantic-guided distillation module that provides auxiliary supervision using pseudo-labels derived from query-document semantic similarities. Extensive experiments on three multi-hop QA datasets and three out-of-domain GraphRAG benchmarks show that \ourmethod consistently outperforms strong baseline methods and generalizes well, which demonstrates the effectiveness of end-to-end GraphRAG with tightly integrated GFM retrieval and LLM reasoning.

cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.34ACL Anthology · ACL 2026

Teaching LLM to be Persuasive: Reward-Enhanced Policy Optimization for Alignment from Heterogeneous Rewards

Xia Zeng, Yihan Chen, Luhui Liu, Chao Luo, Ye Chen, Zhuangzhuoran

原文摘要

We deploy large language models (LLMs) as business development (BD) agents for persuasive price negotiation in online travel agencies (OTAs). The agent must follow a multi-stage Standard Operating Procedure (SOP) and strict guardrails (no over-promising and no hallucinations), while remaining human-like and effective over long, multi-turn dialogues.We propose Reward-Enhanced Policy Optimization (REPO), a reinforcement learning post-training method that combines heterogeneous rewards: a preference-trained reward model (RM), an LLM-as-a-judge (RJ) for nuanced behaviors (e.g., emotional value and SOP compliance), and rule-based reward functions (RF) (mainly regex-based) for deterministic checks on numerics, formatting, and guardrails. In expert consensus evaluation (three human experts; 30 online conversations and 45 curated bad cases), REPO improves average dialogue rating to 4.63 (+0.33 over GRPO) and raises the share of conversations with at least one excellent response to 66.67% (+23.34 pp over GRPO), while achieving a 93.33% bad-case fix rate with 75.56% clean fixes.In a production A/B test on 9,653 real customer conversations (vs. an intent-driven dialogue system), REPO improves response rate by +12.14 pp and task success rate by +5.94 pp (p<0.001).

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1156ACL Anthology · ACL 2026

Collision to Cognition: Hash-Driven Graph Construction for Efficient RAG

Chuang Zhou, Zheng Yuan Hong Kong Polytechnic University, Linhao Luo, Zhaozhuo Xu Workato and Stevens Institute of Technology, Yilin Xiao, Junnan Dong, Siyu An, di Yin Tencent Youtu 等 10 位作者

原文摘要

Retrieval-Augmented Generation (RAG) has long been a promising paradigm for enhancing large language models (LLMs) with external knowledge. Traditional embedding-based methods for graph construction can capture semantic similarity but struggle to establish fine-grained, interpretable logical relationships. Recently, Graph-enhanced RAG (GraphRAG) has gained increasing popularity for its capability in modeling logical relationships. However, graph construction requires extensive token consumption for triple extraction and summarization, making it costly and slow. Accordingly, we propose MeshRAG, a novel framework for mining efficient structures via hashing to enhance RAG. We adopt an inductive paradigm in which global graph structure emerges from local hash collisions rather than explicit symbolic extraction. By replacing neural embedding search with lightweight and bitwise operations, MeshRAG automates a simple and rapid graph construction process. Furthermore, the hash collision mechanism provides transparent evidence for logical connections and retrieval decisions. Experimental results show that MeshRAG outperforms existing baselines, while its graph construction requires no GPU resources or token budget and can structure over ten thousand chunks in a few minutes.

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.1198ACL Anthology · ACL 2026

Attention Basin: Why Contextual Position Matters in Large Language Models

Zihao Yi, Zhenqing Ling Alibaba Group, Delong Zeng, Haohao Luo SUN YAT-SEN UNIVERSITY, Zhe Xu, Wei Liu, Jian Luan Xiaomi Corporation, Wanxia Cao 等 9 位作者

原文摘要

The performance of Large Language Models (LLMs) is significantly sensitive to the contextual position of information in the input. To investigate the mechanism behind this positional bias, our extensive experiments reveal a consistent phenomenon we term the attention basin: when presented with a sequence of structured items (e.g., retrieved documents or few-shot examples), models systematically assign higher attention to the items at the beginning and end of the sequence, while neglecting those in the middle. Crucially, our analysis further reveals that allocating higher attention to critical information is key to enhancing model performance. Based on these insights, we introduce Attention-Driven Reranking (AttnRank), a two-stage framework that (i) estimates a model’s intrinsic positional attention preferences using a small calibration set, and (ii) reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions. AttnRank is a model-agnostic, training-free, and plug-and-play method with minimal computational overhead. Experiments on multi-hop QA and few-shot in-context learning tasks demonstrate that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures.

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.1360ACL Anthology · ACL 2026

Detecting What Queries Seek: Steering LLM Safety with FFN Output Activation Monitoring

Xiaohao Luo Northeastern University, Ying Wei Northeastern University, China, Rui Zhao Northeastern University

原文摘要

Recently, activation steering has attracted considerable attention as a low-cost approach to improving the safety of large language models (LLMs). However, most existing methods apply interventions indiscriminately, often causing excessive refusal of benign queries. Although recent works have begun to explore selective intervention, their intervention decisions typically rely on residual stream activations where information is highly entangled, resulting in limited discriminative power and unreliable interventions. To address this issue, we propose FFN-Guided activation steering (FGAS). Motivated by the observation that feed-forward networks (FFNs) in LLMs serve as core modules for knowledge storage, we propose leveraging FFN output activations as more discriminative signals for intervention, since these activations more explicitly reflect the intent of a query. For a given query, FGAS projects the corresponding FFN output activation into a low-dimensional subspace that effectively separates harmful and benign queries, and then makes precise intervention decisions by assessing its similarity to pre-constructed prototype activations representing harmful and benign classes. Extensive experiments demonstrate that FGAS achieves state-of-the-art defense performance against various jailbreak attacks, while nearly preserving the model’s original performance on benign tasks.

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.1462ACL Anthology · ACL 2026

Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization

Junzhe Wang, Zhiheng Xi, Yajie Yang, Hao Luo, Shihan Dou, Tao Gui Fudan University, Qi Zhang Fudan University

原文摘要

Search agents extend Large Language Models (LLMs) beyond static parametric knowledge by enabling access to up-to-date and long-tail information unavailable during pretraining. While reinforcement learning has been widely adopted for training such agents, existing approaches face key limitations: process supervision often suffers from unstable value estimation, whereas outcome supervision struggles with credit assignment due to sparse, trajectory-level rewards. To bridge this gap, we propose Contribution-Weighted GRPO (CW-GRPO), a framework that integrates process supervision into group relative policy optimization. Instead of directly optimizing process rewards, CW-GRPO employs an LLM judge to assess the retrieval utility and reasoning correctness at each search round, producing per-round contribution weights. These weights are used to rescale outcome-based advantages along the trajectory, enabling fine-grained credit assignment without sacrificing optimization stability. Experiments on multiple knowledge-intensive benchmarks show that CW-GRPO outperforms standard GRPO by 5.0% on Qwen3-8B and 6.3% on Qwen3-1.7B, leading to more effective search behaviors. Additional analysis reveals that successful trajectories exhibit concentrated contributions in specific rounds, providing empirical insight into search agent tasks. Our code is available at https://github.com/zsxmwjz/CW-GRPO.

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.1788ACL Anthology · ACL 2026

NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks

Zhihao Luo Shanghai Artificial Intelligence Laboratory and East China Normal University, Wentao Yan, Jingyu Gong East China Normal University, Min Wang Sensetime, Zhizhong Zhang, Xuhong Wang Shanghai AI Laboratory, Yuan Xie East China Normal University, Xin Tan East China Normal University and Shanghai Artificial Intelligence Laboratory

原文摘要

Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms. In this paper, we observe that both tasks can be formulated as Markov Decision Processes (MDP), suggesting a foundational principle for their unification. Hence, we present NaviMaster, the first unified agent capable of unifying GUI navigation and embodied navigation within a single framework. Specifically, NaviMaster (i) proposes a visual-target trajectory collection pipeline that generates trajectories for both GUI and embodied tasks using a single formulation. (ii) employs a unified reinforcement learning framework on the mix data to improve generalization. (iii) designs a novel distance-aware reward to ensure efficient learning from the trajectories. Through extensive experiments on out-of-domain benchmarks, NaviMaster is shown to outperform state-of-the-art agents in GUI navigation, spatial affordance prediction, and embodied navigation. Ablation studies further demonstrate the efficacy of our unified training strategy, data mixing strategy, and reward design. Resources will be released to the community.

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.1864ACL Anthology · ACL 2026

AdaMix: Adaptive Mixing for Short and Long Reasoning Adapters

Hao Luo Wuhan University, Xiao Yan Wuhan University, Xinyan Li, Qiming Zeng Wuhan University, Yuhao Lin, Shanshan Feng Wuhan University and The Agency for Science, Technology and Research, Hao Wang Wuhan University, Jiawei Jiang Wuhan University

原文摘要

Large Reasoning Models (LRMs) have achieved remarkable success on complex tasks by generating detailed Chain-of-Thought (CoT) reasoning. However, they tend to apply a uniform, computation-intensive deep reasoning strategy to all problems, leading to unnecessary overhead on simple tasks. This significantly hinders their efficiency in real-world applications. While existing methods have improved reasoning efficiency to some extent, they still face critical challenges such as conflicting objectives, limited adaptability. To address these limitations, we propose AdaMix, an adaptive reasoning framework via decoupled optimization. To mitigate optimization conflicts, AdaMix first constructs two specialized adapters: an efficiency-oriented short adapter and an accuracy-oriented long adapter. It then incorporates a difficulty-aware routing model that assesses problem complexity to predict a reasoning intensity coefficient. This coefficient is used to dynamically interpolate a mixed adapter from the two base adapters, enabling fine-grained reasoning control. Our experiment demonstrates that our AdaMix reduces the average response length of DeepSeek-R1-Distill-Qwen-7B by 54.9% while improving accuracy by up to 4.8% on five mathematical datasets, thus indicating a favorable accuracy-efficiency trade-off.

cs.CL

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