Hardware Architecture
Coarse-to-Fine Macro Placement via Evolutionary Search and Critical Macro Tuning
Biao Liu, Zhiping Jin, Kaixuan Sun et al. (+3 more)
Macro placement is a critical stage in chip physical design that substantially affects downstream implementation quality. Recent search-based methods improve existing layouts through partial reconstruction, but quality-biased or spatially restricted macro selection can limit the diversity of reconstruction proposals, potentially hindering escape from local optima. Moreover, coarse-grid representations restrict placement precision. To address these challenges, we propose C2FPlace, a Coarse-to-Fine macro Placement framework that integrates population-based evolutionary search with fine-grained refinement. During coarse-grained optimization, tournament selection chooses promising parents from randomly sampled groups of layouts, and
stochastic partial rip-up and re-place generates offspring by sampling macro subsets across the entire layout. A two-phase schedule samples reconstruction ratios from a higher range early in the search and a lower range later, supporting broad exploration followed by more conservative refinement. During fine-grained optimization, critical macro tuning enables positional adjustments beyond the coarse grid to obtain additional half-perimeter wirelength (HPWL) reduction. Experiments on the ISPD2005
benchmark show that C2FPlace reduces HPWL by 17.82% over EGPlace and 17.86% over RollPlace on average. On the ICCAD2025
benchmark, C2FPlace achieves the best average ranking among the compared methods under the evaluated power, performance, and area (PPA) metrics. Our codes are available in \href{https://github.com/lxxxxb/C2FPlace}{https://github.com/lxxxxb/C2FPlace}.
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All authors Biao Liu, Zhiping Jin, Kaixuan Sun, Zengrui Lu, Qingquan Zhang, Bo Yuan
Computer Vision
Modeling Whole-Slide Images as Dynamic Tumor Microenvironment Fields
Lei Wu, Jiashuai Liu, Di Zhang et al. (+9 more)
Due to the gigapixel-scale nature of whole-slide images (WSIs), weakly supervised WSI analysis is commonly formulated as a multiple instance learning (MIL) problem, where patch-level features are aggregated into slide-level representations. However, diagnostic and prognostic evidence often arises from spatially coherent tumor microenvironment regions and their interactions, rather than isolated patches alone. Existing patch-level or static region-based methods usually overlook how tissue regions should be adaptively formed and subsequently evolved through microenvironment interactions across heterogeneous boundaries. In this paper, we propose Concept-Guided Tumor Microenvironment
Evolution (TMEvolve), a reaction-
diffusion-inspired framework that models WSIs as latent tumor microenvironment fields over discrete patch graphs. TMEvolve instantiates this view as a learnable graph-discretized
evolution process over patch neighborhoods. It first forms adaptive soft tissue regions as coherent microenvironment units, then performs pseudo-time
evolution through two complementary local dynamics: intra-region
diffusion, which stabilizes latent states within coherent tissue compartments, and concept-guided boundary flux, which propagates visual feature signals and
language-derived concept signals across heterogeneous region interfaces. The evolved microenvironment regions are finally aggregated for slide-level prediction. We evaluate TMEvolve on six
datasets across three weakly supervised WSI tasks: survival prediction, gene expression prediction, and histological subtype classification. TMEvolve consistently improves over representative MIL methods, pathology foundation models, and concept-guided baselines. Ablation studies and visualizations further support the effectiveness and interpretability of TMEvolve, highlighting the value of dynamic region modeling and boundary interaction.
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All authors Lei Wu, Jiashuai Liu, Di Zhang, Zhangpeng Gong, Yingkang Zhan, Yi Niu, Jiusong Ge, Chunze Yang, Kai Yi, Mireia Crispin-Ortuzar, Chen Li, Zeyu Gao
Cryptography and Security
ReproBench: Benchmarking LLM Agents on Reproducing Vulnerability From Scratch
Liang He, Sheng Wu, Haomiao Hao et al. (+3 more)
Large
language model (LLM)
agents are increasingly evaluated on cybersecurity tasks such as vulnerability reproduction, exploitation, and patching. However, existing cybersecurity
benchmarks predominantly operate under a post-environment evaluation paradigm, i.e., handing the
agent source code, a container, or an executable binary. This setup bypasses the critical environment reconstruction step, leaving a fundamental question for real-world vulnerability analysis: can an
agent autonomously reconstruct the required execution environment and reproduce a vulnerability entirely from scratch? To address this gap, we present ReproBench, an evidence-grounded
benchmark designed to evaluate
agent capabilities in end-to-end vulnerability reproduction starting from solely a CVE identifier. ReproBench decomposes the full reproduction workflow into six distinct phases, and assesses performance on each phase independently using verifiable experimental artifacts: downloaded firmware images, unpacked binaries, granular analysis logs, and validated crash samples, among others. We instantiate ReproBench with 30 real-world IoT firmware vulnerabilities, which serve as ideal test cases for our from-scratch evaluation setting. Our evaluation demonstrates that 45.3% of test runs resort to vulnerability simulation - a prevalent remediation workaround adopted across all evaluated LLM
agents - while only 5.3% of CVE-model pairs achieve successful reproduction of real-world vulnerabilities. Despite the low overall success rate, these non-trivial successful cases confirm that state-of-the-art LLM
agents already possess the capacity for fully autonomous end-to-end vulnerability reproduction. Concurrently, our in-depth analysis of failed cases identifies core bottlenecks impeding LLM
agents throughout the reproduction pipeline, offering actionable insights for subsequent research.
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All authors Liang He, Sheng Wu, Haomiao Hao, Hongduo Zhao, Jia Yan, Purui Su
Artificial Intelligence
CORTEX: Learning to Share and Specialize in Dense Language Models
Chuiyang Meng, Ming Tang and Vincent W.S. Wong
Large
language models are trained on heterogeneous data mixtures, where different knowledge domains require both shared knowledge and specialization. Existing modular approaches typically impose explicit components or discover modules through interpretability analysis after training. In this work, we propose CORTEX, a learning dynamics-inspired framework that learns internal modularization within dense
language models. CORTEX partitions trainable
matrices into parameter groups and learns module assignments from domain-conditioned gradient and cross-domain gradient similarity. We introduce the selective lesion score and module-domain mutual information to characterize the target-domain lesion effects and
alignment, and analyze how module assignment affects the trade-off between assignment bias and update magnitude. Experiments with 160M, Qwen3-8B, and Qwen3-32B backbone models show that CORTEX achieves the highest synthetic-domain exact match and largest average perplexity reduction, while remaining competitive on real-domain evaluations and forming identifiable modules.
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Networking
WiFi Backscatter for Green Internet of Things: Concepts, Research Trends, and Practical Challenges
Weiqi Wu, Shuo Wang and Zhaoyuan Xu
WiFi backscatter has emerged as a promising technology for green Internet of Things (IoT) connectivity by enabling battery-free devices to communicate through widely available WiFi signals. Despite substantial progress in recent years, a considerable gap remains between research prototypes and practical deployment. This paper provides an overview of WiFi backscatter from the perspective of practical and green IoT systems. We first introduce the fundamentals and key enabling techniques, together with potential IoT applications. We then outline recent research trends toward higher throughput, concurrent communication, simplified deployment, commercial compatibility, and joint communication and sensing. Furthermore, we identify the key challenges that still hinder practical deployment, such as limited transmitter-to-tag operating range and packet loss in frequency-shifted backscatter. We believe that addressing these challenges will be critical to enabling WiFi backscatter to become a practical communication technology for future green IoT systems.
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Computation and Language
Unbiased Top-$k$ Estimation for On-Policy Distillation
Linjian Meng, Siyuan Gan, YuHan Li et al. (+5 more)
On-policy
distillation (OPD) is becoming an important component of large
language model (LLM) post-training for transferring the
reasoning capability of a strong teacher LLM to a weaker student LLM. OPD trains the student by minimizing the reverse KL divergence between the teacher and the student via rollouts generated by the student's policy. However, estimating the gradient of the reverse KL divergence in OPD remains a challenge. Using only the sampled
token from the student-generated rollout is computationally cheap but provides limited distributional supervision, which will degrade accuracy. In addition, using the full vocabulary provides complete distributional supervision but is computationally expensive. Therefore, recent works propose Top-$k$ OPD (TK-OPD) that use selected top-$k$
tokens, which provides richer distributional supervision than sampled-
token estimation at substantially lower computational cost than full-vocabulary estimation. Unfortunately, using only the selected top-$k$
tokens induces bias, leading to accuracy degradation, as the probability mass outside the selected top-$k$
tokens is discarded. To address the bias of TK-OPD, we propose Tail-Corrected Top-$k$ On-Policy
Distillation (TT-OPD). It preserves the advantages of TK-OPD, including rich distributional supervision and low computational cost, while providing an unbiased
estimator of the gradient of the reverse KL divergence. The key insight of TT-OPD is to use not only the selected top-$k$
tokens, but also the sampled
token from the student-generated rollout, thereby recovering the discarded probability mass in expectation, avoiding the bias. Experimental results demonstrate that TT-OPD significantly outperforms other tested OPD variants.
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All authors Linjian Meng, Siyuan Gan, YuHan Li, Xiran Wang, Ziyang Ding, Ditang Gou, Yiming Wu, Zhen Zhao
Machine Learning
Livin' on a Prior: Likelihood Score Approximation for Inverse Problems
Rostislav Makarov, Tal Peer, Danilo de Oliveira et al. (+1 more)
Generative models have found great success as data-driven methods of solving inverse problems. Two popular approaches work either by combining a pretrained generative prior with a known degradation model, or by training a conditional generative model directly from paired data. We target a setting that spans both regimes: unknown degradations can be learned from few paired examples, while known degradations can be learned from self-generated samples. We introduce Likelihood Score Approximation (LSA), a generative framework that keeps a pretrained unconditional model fixed and learns an observation-conditioned model that approximates the likelihood score from paired samples. Within a conditional
stochastic-interpolant framework, LSA can be trained in either score or velocity coordinates, independently of the unconditional model's native parameterization, and supports both deterministic and
stochastic sampling. We further show empirically that the prior model can be swapped post-training while keeping the same LSA model. Across
speech and image inverse problems, LSA operates effectively even at roughly 0.01% of the full training
dataset. On the ImageNet-256
benchmark it achieves competitive or better restoration quality than strong posterior-sampling baselines while requiring up to several orders of magnitude fewer network evaluations.
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All authors Rostislav Makarov, Tal Peer, Danilo de Oliveira, Timo Gerkmann
Sound
Prox-Friendly Log-Magnitude Prior on Complex-Valued Signal
Kazuki Matsumoto, Keidai Arai and Kohei Yatabe
The logarithmic transform is essential in audio signal processing since human auditory perception is approximately logarithmic with respect to magnitude. However, directly incorporating prior knowledge about signals (e.g., harmonic structure) in the log-magnitude domain into optimization problems solved by standard proximal splitting
algorithms remains challenging. To address this issue, this paper proposes a novel regularizer termed EPILOG (Exponential Penalty for Imposing priors on LOG-magnitude). EPILOG indirectly imposes prior knowledge on the log-magnitude of a complex-valued signal through regularization of an auxiliary variable that is shown to be linked with the log-magnitude. Furthermore, we derive its variable-wise proximity operators and develop a proximal splitting
algorithm using these operators. Experiments on
speech dereverberation demonstrate the effectiveness of the proposed regularizer, particularly in promoting cepstral-domain sparsity.
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Artificial Intelligence
Social Circuits behind Multi-agent Echo Chambers
Chuiyang Meng, Wenlu Yu, Ming Tang et al. (+1 more)
Language-model
agents exchange messages to combine evidence, but their communication can also create echo chambers that reinforce shared errors. However, overall task performance does not explain how a message changes the receiving
agent's internal activations and affects its decision. In this work, we introduce Social
Circuits, a framework for tracing message effects through receiver activations. We compare the receiver's answers before and after changing a message. Then, we restore selected activations recorded under the original message to determine how much of the message effect these activations reproduce. Based on Social
Circuits, we propose
Circuit-Guided Deliberation (CGD), which learns to select useful messages using receiver activation changes. We establish when activation replacement preserves receiver decisions and bound the gap between CGD's task performance and the best achievable through message selection. Experiments show that receiver activation changes explain the message effects and guide message selection that improves the task performance. Across three models and four
datasets, CGD achieves the highest or joint-highest average accuracy in our main comparisons while generating fewer
tokens than multi-
agent baselines.
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All authors Chuiyang Meng, Wenlu Yu, Ming Tang, Cheng Li
Machine Learning
Making LLMs Truly Forget: Deep Unlearning by Searching, Selecting, and Severing Knowledge Paths
Jialu Wang, Peizhi Niu, Haoteng Yin et al. (+3 more)
While an unlearned
language model may no longer recall a fact directly, the fact often remains recoverable through multi-hop
reasoning over related knowledge. Most existing unlearning techniques overlook this vulnerability, targeting facts in isolation while leaving their supporting knowledge intact. To achieve true forgetting, we propose a general deep unlearning framework compatible with existing unlearning
algorithms. Our approach adaptively explores both explicit responses and latent internal representations to discover valid
reasoning paths, compiles them into a confidence-aware supporting subgraph, and we apply a graph minimum cut to sever all recovery paths while preserving unrelated knowledge. To rigorously evaluate deep unlearning, we introduce a model-specific pipeline that extracts and completes knowledge graphs from raw text, filtering them by calibrated model confidence to reflect what the model genuinely retains. Comprehensive experiments demonstrate that selectively unlearning supporting knowledge yields substantially deeper forgetting than superficial methods while preserving model utility, highlighting that genuine unlearning requires breaking the relational structures that enable factual reconstruction.
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All authors Jialu Wang, Peizhi Niu, Haoteng Yin, Hans Hao-Hsun Hsu, Pan Li, Rongzhe Wei
Computer Vision
When the Score Becomes the Target: Rethinking Metric Validity in Autonomous Driving
Morui Zhu, Deyuan Qu, Qi Chen et al. (+2 more)
Driving
benchmark scores are increasingly used not only for evaluation but also as optimization targets. This raises a fundamental question: do score gains remain reliable evidence of driving improvement once the score itself is optimized? We address this question by examining how the scoring process responds to changes in driving behavior and whether the resulting gains persist under repeated execution and replanning. We decompose the process into execution, measurement, subscore mapping, and aggregation. Controlled interventions reveal substantial behavioral changes that receive little score response because distinctions are omitted, thresholded, or attenuated between requested and executed motion. Closed-loop comparisons further show that optimization gains can reverse when the execution interface changes, demonstrating their dependence on how requests are executed and returned as feedback. Together, these findings connect the behavioral distinctions preserved by a metric to the conditions under which its gains transfer. Metric validity under optimization therefore requires examining both what the scoring process measures and how the optimized behavior is executed.
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All authors Morui Zhu, Deyuan Qu, Qi Chen, Kentaro Oguchi, Qing Yang
Computer Vision
Clinical Trajectory Alignment for Medical Vision-Language Pre-training
Huimin Yan, Xian Yang, Zhi Wang et al. (+1 more)
Medical
vision-
language pre-training largely follows a visit-level image-report matching paradigm, aligning paired images and reports at individual visits. While effective for static cross-modal correspondence, this paradigm provides limited supervision for longitudinal clinical change, such as whether abnormalities improve, remain stable, or worsen over time. Learning such change is challenging because temporal semantics are implicit in free-text reports, and different abnormalities within the same patient may evolve asynchronously or even in opposite directions. We propose MedCTA, which reframes medical
vision-
language pre-training from visit-level cross-modal matching to learning clinical change. Rather than compressing a patient history into a single temporal representation, MedCTA models clinical change at two complementary scopes. At the abnormality scope, clinically grounded queries construct abnormality-conditioned visual and textual trajectories to capture heterogeneous abnormality
evolution. At the patient-course scope, global image and report sequences are modeled to capture overall clinical progression beyond any individual abnormality. Structured trend supervision is extracted from longitudinal reports by an offline LLM parser, removing the need for manual temporal annotations. Combined with static image-report
alignment, MedCTA learns representations that preserve visit-level cross-modal correspondence while encoding longitudinal change semantics. Experiments on temporal image classification, image-text
retrieval, and zero-shot classification show consistent gains over strong medical
vision-
language baselines.
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All authors Huimin Yan, Xian Yang, Zhi Wang, Liang Bai
Computation and Language
Wanqi Zhou, Jiawei Lu, Yang Wang et al. (+4 more)
Long-term
memory is essential for
language agents to maintain coherent and effective behavior over extended, multi-session interactions. Existing
memory systems mainly use
retrieval at read time, while write-time
memory formation still relies on direct extraction or compression. However, when future information needs are unknown, compressing an entire interaction in one pass can overlook locally important details that may matter later. To this end, we introduce RIME, a
retrieval-induced
memory framework that shifts
memory construction from monolithic compression toward evidence-centered integration. RIME uses generic self-questions to retrieve focused dialogue evidence and grounds
memory formation in both the retrieved evidence and relevant historical
memories, which are jointly reconciled into an evolving
memory bank with temporal and provenance information. At inference time, compressed
memory serves as the primary rather than the sole source of evidence: when it cannot support an answer, RIME retrieves relevant source dialogue together with its local context to recover information omitted during
memory formation, without resorting to full-history processing. Extensive experiments on LoCoMo with Qwen3-235B-A22B and GPT-5.6 Sol show that RIME consistently achieves the best performance across all three quality metrics among the compared methods, while requiring substantially fewer query-time LLM
tokens.
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All authors Wanqi Zhou, Jiawei Lu, Yang Wang, Zhaolong Xing, Zhen Chen, Ai Han, Haoyue Shi
Computational Complexity
Hitting Sets for Polynomials with Small Partial Derivative Spaces
Shubham Bhardwaj and Ramprasad Saptharishi
We give an explicit hitting set of size $\text{poly}(n,d,r)$ for the class of $n$-variate degree-$d$
polynomials whose partial derivative space is bounded by $r$, over any field $\mathbb{F}$ of characteristic zero. In particular, this yields a
polynomial sized hitting set for the class of depth-$3$ powering
circuits. The main technical insight is the construction of a "formal derivation” and properties of the associated Wronskian with respect to this derivation, which was previously studied by Moura [Moura_2004] in a very different context. The proofs in this paper are elementary and completely self-contained. AI disclosure: The proof of this result was obtained during conversations [astra_proof] with OpenAI GPT-6 Astra. The proof presented in this writeup is a rewriting (in the authors' words) of the proof obtained by the AI model in a form that we believe is understandable to researchers.
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Machine Learning
Admissible Diffusion for Multimodal Interventional Trajectories
Xing Han, Shravan Chaudhari, Jiarui Shao et al. (+2 more)
Generating a plausible clinical trajectory does not establish what would happen under a different treatment. We present ADMIT, a framework combining irregular
multimodal representations, treatment-conditioned latent
diffusion and explicit constraints on generated states or actions. We formulate its interventional target through sequential g-computation and distinguish causal assumptions from constraint satisfaction. Its admissibility mechanism translates physiological prior knowledge into explicit constraints on generated states and proposed actions. Treatment-exposure dynamics condition latent transitions, while state projection or action gating applies the constraints during rollout so that they influence subsequent trajectory generation. In our preliminary experiments,
multimodal inputs improved supervised hidden-state recovery and reduced treatment-contrast error. In a simulated dosing-schedule experiment with leak-free history encoding, ADMIT predicted most of the tumor-volume change caused by redistributing a fixed total dose. An exposure input improved these predictions around a temporary dose reduction whether or not the assumed clearance rate was correct, but reduced the predicted size of a dose effect, and a deterministic recurrent baseline matched ADMIT's average predictions. Exposure projection reduced constraint violations, although enforcement remained incomplete. Semi-synthetic experiments using eICU context illustrated treatment-response generation under fixed and adaptive policies. Observational examples further characterize model treatment sensitivity. ADMIT provides a framework for testing whether complementary observations and physiological restrictions improve intervention trajectories, with representation recovery, effect accuracy and rule enforcement assessed separately.
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All authors Xing Han, Shravan Chaudhari, Jiarui Shao, Paul Pu Liang, Suchi Saria
Distributed Computing
Semantics, Workflows, and Infrastructure: Understanding Agent Serving at Production Scale
Yihao Zheng, Jingzhe Jiang, Dejiang Zhu et al. (+4 more)
Large
language model (LLM)
agents execute applications through a workflow of inference requests with tool calls and user interactions. Serving these applications at production scale requires understanding how application behavior shapes inference demand and for guiding efficient execution. Recent characterization studies provide request-level workload measurements and
agent execution analysis. However, an end-to-end view connecting task initiation, workflow execution, and inference infrastructure remains unexplored. In this paper, we analyze a two-week trace of 11.7 million requests from a large-scale production platform for general-purpose
agents, backed by inference infrastructure comprising over 10k GPUs. We characterize the platform at three connected levels: task-level initiation semantics, workflow-level execution patterns, and infrastructure level serving demands. Our measurements reveal workload patterns such as highly skewed request volumes across sessions, rare execution overlap among logical sibling requests, and context reuse across task boundaries. Building on these observations, we analyze deployment implications and identify open problems to guide future research on
agent serving systems.
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All authors Yihao Zheng, Jingzhe Jiang, Dejiang Zhu, Zhiyuan Tan, Yang Tian, Tao Wang, Minchen Yu
Machine Learning
Harmonizing Spectral Evolution in Conditional Flow Matching for TTS
Isha Pandey, Varad Deshpande, Abhijat Bharadwaj et al. (+1 more)
Conditional Flow Matching (CFM) models for text-to-
speech (TTS) suffer from incoherent frequency
evolution during inference. While similar spectral imbalances are addressed in
diffusion models for other domains, those generic solutions fail to generalize to the inherently uncoordinated acoustic dynamics of CFM. We demonstrate that this issue can be effectively mitigated by introducing a novel training-free frequency-selective boosting strategy. Using the Discrete Wavelet Transform (DWT), our method dynamically modulates mel-spectrogram sub-bands during ODE integration, synchronizing spectral development by penalizing aggressive low-frequency growth and boosting lagging high-frequency details. Validated across diverse architectures (Matcha-TTS, F5-TTS, IndicF5), our approach reduces the required Number of Function Evaluations (NFE) from 32 to 26 and improves Frechet Audio Distance (FAD) by up to 61%, all without compromising mean opinion scores, speaker similarity, and
speech intelligibility.
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All authors Isha Pandey, Varad Deshpande, Abhijat Bharadwaj, Ganesh Ramakrishnan
Computer Vision
HUMAN-TCI: Hierarchical Multi-Stream Motion-Aware Network with Torso-Centered Interaction for Text-to-Motion Retrieval
Muhammad Islam, Euijoon Ahn, Usman Naseem et al. (+1 more)
Accurate
retrieval of human motions is a crucial first step in text-guided human motion modeling and synthesis, as it selects semantically relevant sequences from large
datasets and provides grounded references for downstream tasks. Retrieving motions from natural
language descriptions remains challenging because sentences can describe multiple actions, overlapping movements, and intricate dependencies between body parts. Existing methods often focus on simple, single-action descriptions and typically process body parts independently or by merely concatenating features, without explicitly modeling how torso movements influence other parts. In addition, their processing pipelines often rely on computationally heavy models, introducing considerable overhead, particularly when modeling longer or more complex motion sequences. This limits learning discriminative motion-pattern representations, reducing
retrieval accuracy, interpretability, and efficiency in practical applications. To address these limitations, we propose HUMAN-TCI, a Hierarchical Multi-Stream Motion-Aware Network for text-guided human motion
retrieval. HUMAN-TCI employs a three-stream architecture that separately models upper-body, lower-body, and torso motions while explicitly capturing their interactions, allowing torso-related movements to influence the positioning and dynamics of other body parts. By incorporating tailored torso
attention, our model effectively recognizes complex human motion patterns, captures fine-grained motion relationships and handles complex multi-action descriptions. Our framework supports
retrieval for both simple, single-action sentences and long, compositional descriptions containing sequential or overlapping actions without relying on complex models.
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All authors Muhammad Islam, Euijoon Ahn, Usman Naseem, Tao Huang
Computation and Language
AgentHop: A Diagnostic Benchmark for Agentic Multi-Hop Question Answering over Scientific Citation Graphs
Chanhee Park, Jeongho Yoon, Sungbin Han et al. (+2 more)
Agentic tasks require a large
language model to interact with the world, navigating information and gathering evidence across multiple steps with restricted resources. Due to this complexity, agentic task failures arise from various sources, and pinpointing these failure causes is essential to diagnose and improve agentic systems. Existing
benchmarks, however, tend to focus on a single leaderboard score, leaving the underlying failure modes opaque. To fill this gap, we introduce AgentHop, a diagnostic
benchmark of 1,011 multiple-choice questions paired with a controlled seven-tool sandbox under fixed
token, turn, and tool-call constraints. AgentHop reveals model vulnerabilities by dissecting a single accuracy score along four axes of
agent operation:
retrieval, synthesis, tool-call, and resource management. Across 19 models, we find that behavior clusters by model family, with tool-call signatures revealing distinct family fingerprints: GPT models commit early, Anthropic and GLM checkpoints verify before committing, DeepSeek and Kimi over-search, and Gemini-3 Pro stays balanced. Decomposed axes further expose within-family structure: Claude Opus 4.6 and Sonnet 4.6 land within one accuracy point yet diverge on
retrieval-versus-synthesis emphasis, with Opus retrieving more and Sonnet synthesizing better. We release the full
benchmark set and the harness to support diagnostic
agent benchmarking.
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All authors Chanhee Park, Jeongho Yoon, Sungbin Han, Hyeonseok Moon, Heuiseok Lim
Machine Learning
LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization
Shihao Zhang, Weiting Liu, Siyu Shao et al. (+4 more)
Scaling LLM-based optimization from textbook-scale instances to real-world, industrial tasks remains a critical open challenge. Existing approaches are predominantly evaluated on small, self-contained textual problems and often commit to a solver-integrated paradigm, limiting their ability to handle the scale and structural diversity of practical optimization workloads. In this work, we propose a practical framework for training open-source LLMs to tackle real-world, industrial-scale optimization. We first show empirically that solver-integrated
reasoning, exact combinatorial
algorithm, and heuristic search exhibit complementary strengths across different problem structures and scales. Motivated by this, we introduce Strategy-Diverse Reinforcement Learning (SDRL), which trains LLMs as adaptive optimization meta-solvers. SDRL leverages this complementarity through a correctness-gated hierarchical diversity reward that promotes robust exploration across varying strategies and within each strategy, effectively preventing premature strategy collapse. We further introduce a mixed-format training scheme that jointly supports both self-contained textual problems and file-grounded instances. Across comprehensive evaluations, our framework outperforms existing fine-tuned methods and frontier models including DeepSeek-V4-Pro and GPT-5.5, both on average across
benchmarks and on industrial-scale optimization tasks.
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All authors Shihao Zhang, Weiting Liu, Siyu Shao, Yitian Chen, Jianfeng Feng, Dongdong Ge, Yinyu Ye
Machine Learning
Q-learning Penalized Transformer for Safe Offline Reinforcement Learning
Shengchao Hu, Peng Wang, Jifeng Hu et al. (+5 more)
This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an offline
dataset. This problem is inherently challenging as it requires balancing three highly interconnected and competing objectives: satisfying safety constraints, maximizing rewards, and adhering to the behavior regularization imposed by the offline
dataset. To tackle this trilogy challenge, we propose Q-learning Penalized
Transformer policy (QPT), a training–inference consistent framework that bridges conditional sequence modeling with constraint-aware value estimation. QPT trains a
Transformer policy that generates actions conditioned on trajectory context and target return/cost, retaining strong behavior regularization. To inject explicit safety semantics during learning, we augment sequence-model training with a Q-shaped penalty using learned reward and cost Q-functions to favor high return under low constraint violation. At inference, the same Q-functions enforce the cost threshold and choose the highest-reward feasible action, closing the loop between training and deployment. We provide a principled analysis under stylized near-deterministic CMDPs, characterizing how Q-penalized conditional generation improve safety and performance. Empirically, QPT consistently outperforms strong safe offline RL baselines across 38 tasks on the DSRL
benchmark, and exhibits robust zero-shot adaptation to different constraint thresholds.
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All authors Shengchao Hu, Peng Wang, Jifeng Hu, Qiyang Zhou, Anning Hu, Li Shen, Ya Zhang, Dacheng Tao
Computation and Language
Zero-Shot Cue-Grounded Topic Segmentation of Spoken Documents
Suhwan Choi, Myeongho Jeon and Myungjoo Kang
Topic
segmentation structures spoken documents into coherent sections, facilitating
navigation and downstream understanding. The appropriate granularity can vary substantially, ranging from broad thematic shifts to fine-grained subtopics. Existing LLM-based segmenters, however, often struggle to adapt to this variation, causing them to either merge distinct subtopics or over-segment coherent themes. To address this, we introduce Cue-Grounded
Segmentation (CGS), a training-free framework that operates without any task-specific supervision. CGS first identifies phrases that explicitly signal the start of a new topic and uses their sentence positions as segment boundaries. When such cues are insufficient, it falls back to semantic
segmentation, guided by the document structure inferred during cue extraction. Across six
benchmarks and six LLM backbones, CGS consistently outperforms existing baselines, remains robust to noisy ASR transcripts, and achieves these gains with low API cost on proprietary models.
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Computational Engineering
Construction-Reuse Trade-offs for Exact Certificates in Fixed-Rank Threshold Screening
Zisu Li and Jingyang Du
Repeated threshold queries may reuse selected identities without reusing stale reports, but cheaper certificates need not shorten the complete response. We study selected-lower, atomic-upper (SLA) certificates for fixed-rank conjunctive screening with explicit missing-information semantics. An endpoint characterization and a counterexample separate same-source containment from policy-dependent online behavior. The original 320-session experiment reduces summed construction medians by 31.18% against an exclusion-cover certificate, yet its Cover/SLA full-API
geometric time ratio is 0.9682 (95% conditional blocked interval 0.9593-0.9769), and SLA takes 12.94% more summed time than uncached Bitmap. Three separately launched complete repeats preserve this adverse ordering, with Cover/SLA ratios of 0.9665-0.9718. An additional 720-configuration exploration varies catalogue size, construction period, requested count and query locality on empirically resampled tables. SLA is faster in 295 configurations against Cover and 271 against Bitmap, descriptive counts that do not establish universal superiority. Separately instrumented additive costs distinguish construction savings from
retrieval and report costs. A plane-stress component case adds an independent analytical displacement check and 4,608 boundary-challenging queries: five implementations agree exactly, while medium- and fine-mesh selections differ at 459 positions. A state-stratified public bolt-record exercise preserves 185 incomplete positions among 1,479 requests. Raw timings, complete configuration results and a tested clean-environment package support reproducibility. The SLA construction was explored and refined through the self-evolving AI system ZiYor; the named authors specified, implemented and evaluated it. This is a bounded mechanics-to-query study, not physical joint qualification or universal speedup.
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Machine Learning
On the Relation Between Interval Regret and Dynamic Regret
Yi-Han Wang, Peng Zhao and Zhi-Hua Zhou
Non-stationary online learning has attracted much
attention in recent years, as static regret is insufficient to guide
algorithm design in changing environments. To address this limitation, interval regret and dynamic regret have been introduced as two representative performance metrics that strengthen static regret in complementary directions. Interval regret requires an online
algorithm to achieve competitive static regret over every local time interval, whereas dynamic regret evaluates performance against an arbitrary sequence of time-varying comparators. Despite their importance, the relation between these metrics has long remained unclear. Prior work has often regarded interval regret as the stronger notion, based on the intuition that local guarantees should naturally induce global guarantees. Consequently, it is widely conjectured that an
algorithm with optimal interval regret should automatically attain optimal dynamic regret. In this paper, we first establish a negative result that refutes this intuition of a metric-level implication. Specifically, for both convex and curved functions (including exp-concave and strongly convex functions), we show that there exist instances in which an
algorithm with optimal interval regret nevertheless fails to achieve optimal dynamic regret. We then show how to leverage local adaptivity to obtain optimal dynamic regret. In particular, optimal dynamic regret can be attained by invoking an interval regret minimization process over an enlarged Euclidean ball containing the original convex feasible domain and using a suitable domain-converted surrogate loss. This reduction applies to both convex and curved functions. As a byproduct, we obtain the first proper and efficient
algorithm with optimal dynamic regret for exp-concave functions, improving prior results while significantly simplifying the analysis.
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Machine Learning
Coding Agent Memory Post-training: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via Reinforcement Learning
Lirui Luo, Kelong Mao, Heming Xia et al. (+10 more)
Language-model
agents increasingly tackle long-horizon tasks whose interaction histories exceed the model's active context. Recent work has begun to use reinforcement learning to make
memory control part of the policy, often relying on predefined
memory tools within domain-specific training environments of relatively short horizons. This setup ties learned
memory behavior to environment-specific interfaces that lie outside the base model's pre-training and must be learned from scratch, so even after post-training,
agents struggle to use
memory in long-horizon tasks. To address these limitations, we introduce Coding
Agent Memory Gym (CAMG), a suite of long-horizon agentic-RL environments spanning Shop, Coding, DeepResearch, and AutoResearch. Alongside each environment's native task interface, CAMG provides executable shell access and an episode-persistent workspace, enabling
agents to create, revise, search, and reuse files as
memory throughout an episode. We also introduce CAMG-RL, which trains a single policy jointly across all four environments with fully asynchronous PPO, learning this file-based
memory behavior directly from downstream task reward, and we train CAMG-RL-4B and CAMG-RL-9B from Qwen3.5 models of matching size. On SWE-bench Verified and MLE-bench Lite, CAMG-RL-4B and CAMG-RL-9B are competitive with Qwen3.5-35B-A3B and Qwen3.5-122B-A10B, respectively.
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All authors Lirui Luo, Kelong Mao, Heming Xia, Rongqing Li, Xinwei Yang, Luyu Chen, Kieran Wong, Yudong Guo, Xinrui Wang, Jiayin Zhu, Simiu Gu, Sulong Xu, Cong Fang
Machine Learning
MegaGraph: Towards Efficient Training of Large-Scale Graph Transformers with Automated Hybrid Parallelism
Tong Qiao, Ao Zhou, Yingjie Qi et al. (+2 more)
Graph
Transformers (GTs) offer superior representation capabilities by overcoming the depth limitations and over-smoothing issues of traditional Graph Neural Networks (GNNs). However, scaling GTs to large graphs poses critical bottlenecks. Specifically, the
attention score
matrix and its associated
topology-aware bias
matrix jointly incur significant per-layer
memory overhead, and heavy graph
embedding layers result in severe workload imbalances. These characteristics are unique to GT training and are not addressed by parallelism techniques designed for either conventional GNNs or
Transformers, making a dedicated solution necessary. This paper introduces MegaGraph, the first automated hybrid parallelism framework designed for efficient GT training. MegaGraph designs three specialized strategies, namely graph-aware context parallelism, heterogeneous pipeline parallelism, and hybrid data parallelism, to support efficient training on large-scale graphs. However, coordinating these three parallelism strategies yields an exponentially large configuration space. To address this complexity, an automatic search engine leverages precise cost models via a Profile - Model - Search workflow to identify the optimal parallelism configuration. Evaluations demonstrate that MegaGraph enables training on large-scale graphs where state-of-the-art baselines fail due to out-of-
memory (OOM) errors. The framework reduces per-device peak
memory by up to 77.8% and achieves up to 4.51$\times$ training speedup while maintaining model accuracy.
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All authors Tong Qiao, Ao Zhou, Yingjie Qi, Chunming Hu, Jianlei Yang
Artificial Intelligence
Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation
Hanzhong Zhang, Jindong Wang and Siyang Song
Automatic human-like facial reaction generation (FRG) is essential for building intelligent systems that can engage in human-computer interaction (HCI). While diverse and context-appropriate facial reactions can reflect latent appraisal and affective processes in human interaction, most existing FRG methods rely on end-to-end architectures that directly map speaker behaviours to listener expressions without an explicit intermediate internal state. We reformulate FRG as generation mediated by a structured internal-state process and propose the Intentional
Agent, which shifts FRG from direct stimulus-response mapping to stimulus-grounded generation through explicit intermediate states. To represent temporal internal-state
evolution, we propose an internal dynamics model that integrates emotional drives with an iterative Inner Thought Flow (ITF) within a structured intermediate state used for subsequent generation. This state can continue to update during conversational silences. Furthermore, to bridge abstract internal states with physiological actions, we formulate FRG as a downstream affective mapping from this latent thought flow to facial expressions. Experiments on the REACT 2025
dataset show an FRDist of 72.39 and an FRDiv of 0.5057; perceptual plausibility is evaluated separately through blinded human ratings. A blinded human evaluation of 96 reactions found no significant difference in mean score between Full and ground truth ($5.527$ vs.\ $5.195$, $p_{\mathrm{Holm}}=.076$), while Full significantly outperformed Event-Triggered and Heuristic-Only (both $p_{\mathrm{Holm}}<.001$). The Reaction Quality Scorer (RQS) correlated strongly with human judgements (Pearson $r=.855$; Spearman $\rho=.821$, both $p<.05$), supporting its use as an automatic metric. These results underscore the immense potential of endogenous dynamics in building highly autonomous, human-like
agents.
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Artificial Intelligence
OSPD: On-Policy Self-Distillation for Persona-Consistent Dialogue
Rui Xu, Yikai Zhang, Aili Chen et al. (+3 more)
Maintaining persona consistency across multi-turn dialogues remains a core challenge for role-playing
language models. Off-policy
distillation from external teachers incurs distribution mismatch that compounds across dialogue turns, while reinforcement learning struggles with reward ambiguity inherent in subjective persona fidelity. We propose OSPD, an on-policy self-
distillation framework where the same model serves as both teacher and student under asymmetric information: the teacher receives a complete character profile while the student sees only a brief summary, and the student generates trajectories from its own policy. We find that teacher confidence in role-playing dialogue exhibits a bimodal structure—sharply peaked at character-critical
tokens yet diffuse at generic utterances—and introduce role-aware divergence switching to match this structure. A progressive trait masking curriculum further forces staged internalization of character knowledge along semantic dimensions. Experiments on CharacterBench, CharacterEval, and SocialBench show that OSPD substantially improves persona consistency over supervised fine-tuning and multi-turn RL baselines, without requiring any external teacher or reward model.
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All authors Rui Xu, Yikai Zhang, Aili Chen, Zicheng Zhao, Xu Yinghui, Libo Wu
Machine Learning
PROACT-Agent: Progressive Runtime Oversight and Active Circuit-breaking for Real-Time Safety
Ding Jia, Wei Liu, Xianglong Du et al. (+5 more)
The transition from Large
Language Models (LLMs) to
agents shifts safety stakes from toxic text to irreversible environmental harm. While current defenses remain largely retrospective, proactive runtime intervention is bottlenecked by the lack of large-scale, causally-consistent data. We propose PROACT-
Agent, a framework for synthesizing high-fidelity trajectories to enable real-time guardrails. We identify a critical "safety drift" in prior
benchmarks, where lenient annotation paradigms fail to enforce temporal consistency. PROACT-
Agent addresses this through: (1) Progressive Trajectory Unrolling to reveal risks hidden in long-context interactions; (2)
Reasoning-Augmented Causal Rectification to enforce monotonic causal consistency; and (3) Culturally-Aware Data Localization for cross-border robustness. We introduce PROACT-Bench, a bilingual safety
benchmark with 155,780 states labeled through multi-model adjudication. Evaluating updated context before the next LLM inference, the trained guard achieves 91.46% unsafe-class F1 and 90.63% exact-boundary detection under complete source holdout. In AgentDojo, it reduces non-DoS targeted attack success from 20.82% to 0.40%.
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All authors Ding Jia, Wei Liu, Xianglong Du, Yingjie Li, Yingqing Yang, Huili Yu, Zhangsong Zhan, Chu Zhou
Robotics
From World Models to World Action Models: Rethinking Next-State Prediction
Tingyu Yuan, Ziming Ji, Biaoliang Guan et al. (+9 more)
Predicting the next state is a core paradigm of World Models for modeling physical dynamics, emphasizing prediction fidelity. As World Models evolve into World-Action Models (WAMs), existing methods still fix the next state before training as RGB, a single latent feature, or a static combination of predefined targets, thereby constraining action learning to the inductive biases preserved by a particular representation. To address this limitation, we propose CF-WAM, a dynamic next-state prediction framework that samples visual, semantic,
geometric, and interaction projections of the same future, standardizes them into a common
video form, and supervises a unified WAM across these projections. The action-relevant constraints exposed by these projections accumulate across training steps, forcing WAM to capture the underlying state-transition structure that supports multiple projections of the same action-conditioned future. This dynamic mechanism also provides a natural cross-embodiment dynamics reference frame for Human and
Robot learning. By jointly learning across different next-state parameterizations, heterogeneous Human and
Robot experience can bypass appearance differences and directly contribute to shared state-transition learning, improving cross-embodiment generalization. Experiments show that CF-WAM improves both training efficiency and final control performance, while translating Human experience effectively into policy gains. CF-WAM achieves state-of-the-art performance on RoboCasa-GR1 with an average success rate of 82.50%, while reaching 82.65% on LIBERO-Plus and up to 84.00% in real-world evaluations.
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All authors Tingyu Yuan, Ziming Ji, Biaoliang Guan, Wen Ye, Wenrui Tian, Zhaopeng Gu, Feihong Zhang, Xu Yang, Yan Huang, Zhaowen Li, Chaoyang Zhao, Jinqiao Wang