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General Machine Learning · Evaluation

Xiaoyuan Liu, Tianneng Shi, Wenbo Guo, Dawn Song

Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most benchmarks rely on fixed, LLM-centric harnesses that require heavy integration, create test-production mismatch, and limit fair comparison across diverse agent designs. This position paper argues that the root problem is the lack of an open, agent-agnostic assessment interface. We advocate Agentified Agent Assessment (AAA), where evaluation is performed by assessor agents and all participants interact through standardized protocols: A2A for task management and MCP for tool access. This design separates assessment logic from agent implementation and enables reproducible, interoperable, and multi-agent evaluation. We further introduce AgentBeats as a concrete realization of AAA: we identify five practical operation modes that make standardized assessment compatible with real-world constraints on openness, privacy, and deployment; we provide recommended practices that allow both agent developers and benchmark designers to adopt AAA with minimal additional effort; and we show how this approach turns agent evaluation from ad-hoc integration work into a reusable, portable, and production-aligned process. Together, AAA and AgentBeats offer a clear path toward open, standardized, and reproducible agent assessment.

Social Aspects · Everything Else

Resmi Ramachandranpillai, Thulasi Tholeti, Tomo Lazovich, Ricardo Baeza-Yates

Many failures of deployed machine learning systems stem not from insufficient accuracy, but from neglecting responsibility as a core design requirement. While responsibility principles are widely studied, they are often treated as post-hoc checks rather than as integral factors of system design. This framework has reinforced the perception that responsible practices inherently trade-off with model performance. In this position paper, we challenge that assumption and argue that responsibility and performance are not inherently at odds. We adopt a lifecycle-oriented perspective, identifying which responsible AI principles are most critical at each stage, from problem formulation and data curation to training, deployment, and monitoring. Drawing on real-world instances, we show how misaligned choices at specific stages can compound downstream risks and how alternative design choices could have mitigated these failures. We argue that responsible AI should be understood as a system design challenge rather than a constraint, and we offer operational guidance for integrating responsibility into mainstream machine learning workflows in a way that supports, rather than undermines, real-world performance.

Deep Learning · Large Language Models

Haodong WANG, Junjie Liu, Zicong Hong, Qianli Liu, Jian Lin, Song Guo, Xu Chen

4-bit quantization reduces the memory footprint and latency of large language model inference, but its aggressive precision reduction can severely degrade accuracy. Prior methods address this by decomposing each weight matrix into two components (e.g., via singular value decomposition) and quantizing them separately, assigning the bulk of values to a low-precision residual component while handling outliers with a high-precision low-rank component. However, such decompositions are designed to minimize the real-valued energy of the residual, rather than the post-quantization error of the residual and low-rank components. We propose TwinQuant, a 4-bit quantization framework that learns quantization-friendly decomposed subspaces and jointly reshapes both the low-rank and residual components. TwinQuant learns component-specific transformations via a joint optimization over the Stiefel and general linear manifolds, flattening their distributions and reducing dynamic-range imbalance. To enable efficient end-to-end execution, we further design a fused dual-component kernel that pipelines the two-stage low-rank computation on-chip and merges both components with a single epilogue, avoiding intermediate global-memory traffic. Across LLaMA3 and Qwen3 models, TwinQuant preserves near-FP16 accuracy and delivers up to $2.11\times$ end-to-end speedup over an FP16 baseline.

Applications · Everything Else

Zhiren Gong, Tiantong Wu, Jiaming Zhang, Fuyao Zhang, CHE WANG, Yurong Hao, Yikun Hou, Foo Ping, Yilei Zhao, Fei Huang 等

Large Language Models (LLMs) are increasingly deployed for knowledge synthesis, yet their capacity for compositional generalization in scientific knowledge remains under-characterized. Existing benchmarks primarily focus on single-turn restricted scenarios, failing to capture the capability boundaries exposed by real-world interactive scientific workflows. To address this, we introduce XDomainBench, a diagnostic benchmark for interactive interdisciplinary scientific reasoning. We formalize the composition order and mixture structure to enable systematic stress-testing from single-discipline to inter-disciplinary, comprising 8,598 interactive sessions across 20 domains and 4 task categories, with 8 realistic trajectory patterns covering difficulty and domain-mixture dynamics, simulating real AI4S scenarios. Large-scale evaluation of LLMs reveals a systematic reasoning collapse as composition order increases, stemming from two root causes: (i) direct difficulty increases induced by domain composition, and (ii) indirect interaction-amplified failures where trajectory patterns trigger error accumulation, reasoning breaks, and domain confusion, ultimately leading to session collapse.

Deep Learning · Large Language Models

Tianxiang Chen, Jingyuan Zhou, Longhao Yan, Kaidi Yang

Existing decoding-time safety interventions are often reactive, relying on local signals to correct unsafe outputs after they emerge. Under adversarial prompts that drive generation into recurring unsafe response, such local signals provide weak guidance for stable repair. As a result, rollback and post-hoc rewriting often trade-off response quality with recurrent violations. To address these limitations, we propose RBCBF, a rollback-based decoding-time framework that jointly selects intervention steps and performs distribution-level corrective control. Our key innovation is a risk-aggregation formulation that views terminal violations as the accumulated build-up of risk along the prefix. By selecting rollback steps from these decisive prefixes, RBCBF moves rollback targeting beyond heuristic cues and turns it into a trajectory-level decision. RBCBF then applies invasive corrective control to the next-token distribution under multiple rule constraints. Across jailbreak-style evaluations, RBCBF outperforms prior rollback methods and decoding-time baselines, reducing harmful responses and substantially lowering violation recurrence.

Xiongkun Linghu, Jiangyong Huang, Baoxiong Jia, Siyuan Huang

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key paradigm for unlocking complex reasoning in Large Language Models (LLMs), yet its potential in 3D scene understanding remains untapped. To bridge this gap, we present Reinforcement Fine-Tuning for Video-based 3D Scene Understanding (3D-RFT), the first framework to extend RLVR to 3D perception and reasoning. Our pipeline operates in two stages: activating 3D-aware Multi-modal Large Language Models (MLLMs) via Supervised Fine-Tuning (SFT), followed by reinforcement fine-tuning using Group Relative Policy Optimization (GRPO) with strictly verifiable reward functions. We design task-specific rewards—such as 3D IoU and F1-score—to provide deterministic signals for spatial alignment. Extensive experiments demonstrate that 3D-RFT achieves state-of-the-art performance on video-based 3D scene understanding benchmarks, significantly outperforming VG LLM-8B on detection and grounding tasks. Moreover, our model surpasses larger mainstream models on VSI-Bench, demonstrating the efficiency of verifiable reinforcement learning. We conclude by offering valuable insights into optimal training strategies .

Reinforcement Learning · Deep RL

Yuexuan Wang, Jingyuan Zhou, Kaidi Yang

Hierarchical decision-making frameworks are pivotal for addressing complex control tasks, enabling agents to decompose intricate problems into manageable subgoals. Despite their promise, existing hierarchical policies face critical limitations: (i) reinforcement learning (RL)-based methods struggle to guarantee strict constraint satisfaction, and (ii) optimal control (OC)-based approaches often rely on myopic and computationally prohibitive formulations. To reconcile these trade-offs, hierarchical RL-OC architectures have emerged as a promising paradigm. However, the formulation of the lower-level optimization within these frameworks remains underexplored, often relying on heuristic or myopic objectives. In this work, we propose a principled framework that systematically integrates upper-level goal abstraction with structured lower-level decision making. We adopt an inverse optimization approach to inform the structure of the lower-level problem from expert demonstrations, ensuring that the objective of the lower-level policy remains aligned with the overall long-term task goal. To validate the approach, our framework is evaluated on distinct decision making tasks: network-based resource allocation and continuous collision avoidance. Empirical results demonstrate that our method consistently outperforms strong baselines based on end-to-end RL, learning-augmented optimal control, and existing hierarchical RL approaches in both efficiency and decision quality.

Social Aspects · Robustness

Jingyuan Zhou, Yuexuan Wang, Kaidi Yang

Ensuring scalable input-to-state stability (sISS) is critical for the safety and reliability of large-scale interconnected systems, especially in the presence of communication delays. While learning-based controllers can achieve strong empirical performance, their black-box nature makes it difficult to provide formal and scalable stability guarantees. To address this gap, we propose a framework to synthesize and verify neural vector Lyapunov-Razumikhin certificates for discrete-time delayed interconnected systems. Our contributions are three-fold. First, we establish a sufficient condition for discrete-time sISS via vector Lyapunov-Razumikhin functions, which enables certification for large-scale delayed interconnected systems. Second, we develop a scalable synthesis and verification framework that learns the neural certificates and verifies the certificates on reachability-constrained delay domains with scalability analysis. Third, we validate our approach on mixed-autonomy platoons, drone formations, and microgrids against multiple baselines, showing improved verification efficiency with competitive control performance.

Applications · Computer Vision

Xiaotao Wang, Yiyang Fang, Wenke Huang, Bin Yang, Guancheng Wan, Mang Ye

Multimodal sentiment analysis aims to infer human emotions by integrating signals from diverse modalities. However, missing modalities are common in real-world applications due to sensor failure, data corruption, or privacy concerns. Existing approaches typically follow two main paradigms: recovery-based and non-recovery-based methods. This dichotomy results in two critical limitations: I) computational inefficiency and semantic inconsistency (recovery-based methods rely on heavy generators that incur prohibitive inference latency and risk semantic drift due to lack of class-level priors); II) lack of instance specificity (non-recovery-based methods rely on static global mappings that fail to capture sample-specific affective cues). To address these gaps, we propose Adaptive Prototype Imputation (API). To mitigate I), we introduce *Semantic-anchored Class-Temporal Prototype Estimation (SCOPE)* to construct non-trainable prototypes as stable semantic anchors, ensuring semantic reliability. To resolve II), we design *Directional Instance-Adaptive Affine Modulation (DIAM)* to dynamically modulate these anchors via direction-specific affine transformations, capturing instance-unique affective characteristics without generative overhead. Experimental results on CMU-MOSI and CMU-MOSEI demonstrate that API outperforms state-of-the-art baselines, establishing a robust and lightweight prototype-centric paradigm for multimodal sentiment analysis.

Applications · Health / Medicine

Tan Pan, Shuhao Mei, Yixuan Sun, Kaiyu Guo, Chen Jiang, Zhaorui Tan, Mengzhu Li, LIMEI HAN, Xiang Zou, Yuan Cheng 等

Self-supervised pre-training methods in medical imaging typically treat each individual as an isolated instance, learning representations through augmentation-based objectives or masked reconstruction. They often do not adequately capitalize on a key characteristic of physiological features: anatomical structures maintain consistent spatial relationships across individuals (instances), such as the thalamus being medial to the basal ganglia, regardless of variations in brain size, shape, or pathology. We propose leveraging this cross-instance topological consistency as a supervisory signal. The challenge arises from the inherent variability in medical imaging, which can differ significantly across instances and modalities. To tackle this, we focus on two alignment regimes. (i) Intra-instance: with pixel-level correspondences available, a cross-modal triplet objective explicitly preserves local neighborhood topology. (ii) Inter-instance: without such supervision, we derive pseudo-correspondences to control partial neighborhood alignment and prevent topology collapse across modalities. We validate our approach across 7 downstream multi-modal tasks, achieving average improvements of 1.1\% and 5.94\% in segmentation and classification tasks, respectively, and demonstrating significantly better robustness when modalities are missing at test time.

Deep Learning · Large Language Models

Zhiren Gong, Yikun Hou, Fan Wu, CHE WANG, Fuyao Zhang, Tiantong Wu, Yurong Hao, Jiaming Zhang, Yiyang Duan, Tiantong Wang 等

Large-scale dedicated application of LLMs in diverse scenarios increasingly demands specialized model inference behavior under strict constraints of accuracy, latency, and memory. However, the heterogeneous and long-tailed nature of real-world specialized scenarios makes it difficult to obtain training data and optimize models. We study a practical inference-time specialization setting: given an LLM base, we compile a reusable, budget-bounded pathway/subnetwork within a specific scenario. Our approach is motivated by an empirical coupling phenomenon: input scenario sets aligned with similar representation subspaces (e.g., domain) in embedding space tend to activate a consistent and sparse set of internal reasoning pathways in model parameter space. To build the bridge between them, we propose probe-based SubspacePath Pruner with two core components: (1) Domain-Basis Synthesis (DBS) constructs a quasi-orthogonal basis of domain axes in embedding space, serving as a stable coordinate system. (2) Probe-based Scenario Pruning (PSP) uses efficient layer-wise linear probes to estimate axis alignment and compute budgeted head-wise pathways for a specific scenario. Experiments on LLaMA-2-13B show 29.3 average Recall on cross-domain tests (vs. 24.7 dense) and 21.6 on cross-dataset tests (vs. 25.5 dense) with 1.27x speedup at ~30% pruning ratio.

General Machine Learning · Clustering

Yuemeng Huang, Huibing Wang, Jinjia Peng, Lu Jiang, Qian Liu, Zetian Mi, Jiqing Zhang

Anchor-based multi-view clustering has garnered wide attention for its ability to reduce the computational complexity of large-scale spectral clustering.However, existing methods mostly adopt a unidirectional optimization paradigm confined to sample-anchor bipartite graphs, treating the construction of the consensus graph and discrete clustering assignments as separate sub-problems to be solved independently. This weakens the information exchange between continuous representation and discrete structure, confining the optimization process to iterative updates within local modules.To address these limitations, we propose a Discretely-Refined Multi-view Clustering(DRMC) via Aligned Anchor Learning. Unlike approaches that directly perform fusion in the anchor space, our method starts from the anchor graph, elevates sample-anchor associations to sample-level similarity graph representations, and thereby enhances both within-cluster similarity and between-cluster separation. Furthermore, we design a discrete feedback module that jointly conducts spectral embedding learning and discrete label assignment by orthogonally aligning the continuous embedding matrix with the discrete indicator matrix. The resulting discrete partition is then fed back into the consensus graph construction, continuously refining the graph structure. Experiments on multiple benchmark datasets demonstrate that the proposed method exhibits significant advantages over existing state-of-the-art approaches.

Deep Learning · Foundation Models

Ethan Roland, Murat Cubuktepe, Erick Martinez, Stijn Servaes, Keenan Pepper, Michael Vaiana, Diogo de Lucena, Judd Rosenblatt, Addie Foote, Cem Anil 等

AI developers face a dual-use dilemma. The same capability that helps one user cure a disease can help another synthesize one. This dilemma could be resolved by access control, granting different users access to different AI capabilities. A gold standard for access control would be to serve models with different capabilities to different users. However, training and deploying multiple models is prohibitively expensive. We address this challenge by developing gradient-routed mixture-of-experts (GR-MoE), a pretraining method that selectively updates experts to induce specialization. Ablating an expert at inference time removes its capability, approximating a model trained on filtered data. We evaluate GR-MoE on synthetic stories and realistic dual-use data spanning biology, cybersecurity, nuclear physics, and code. On realistic data, GR-MoE preserves performance on retained capabilities while achieving 30% compute efficiency on forget capabilities. GR-MoE limits recovery more effectively than post-hoc unlearning and preserves capabilities better than LoRA. GR-MoE's advantages improve when scaled from 48M to 2B parameters, approaching multiple data filtered models in a single training run.

Melissa Pan, Negar Arabzadeh, Riccardo Cogo, Yuxuan Zhu, Alexander Xiong, Lakshya A Agrawal, Huanzhi Mao, Emma Shen, Sid Pallerla, Liana Patel 等

LLM-based agents already operate in production across many industries, yet we lack a clear understanding of which technical methods make these deployments successful. We present the first systematic study of Characterizing Agents in Production (CAP) using first-hand data from agent developers. We conducted 20 in-depth case studies through interviews and surveyed 306 practitioners across 26 domains. We examine why organizations build agents, how they build them, how they evaluate them, and the key challenges they face in deployment. Our findings show that production agents rely on simple, controllable approaches: 68% execute at most 10 steps before human intervention, 70% rely on prompting off-the-shelf models rather than weight tuning, and 74% depend primarily on human evaluation. Reliability—defined as consistent correct behavior over time—emerges as the dominant challenge, which practitioners address through system-level design choices. CAP documents the current state of production agents, providing the research community with visibility into real-world deployment practices and underexplored research opportunities.

Mikael Henaff, Scott Fujimoto, Michael Matthews, Michael Rabbat

Hierarchical reinforcement learning (RL) has the potential to enable effective decision-making over long timescales. Existing approaches, while promising, have yet to realize the benefits of large-scale training. In this work, we identify and solve several key challenges in scaling online hierarchical RL to high-throughput environments. We propose Scalable Option Learning (SOL), a highly scalable hierarchical policy gradient algorithm which achieves a ~35x higher throughput compared to existing hierarchical methods. To demonstrate SOL's performance and scalability, we train hierarchical agents using 30 billion frames of experience on the complex game of NetHack, significantly surpassing flat agents and demonstrating positive scaling trends. We also validate SOL on MiniHack and Mujoco environments, showcasing its general applicability.

General Machine Learning · Data

Melissa Pan, Negar Arabzadeh, Riccardo Cogo, Yuxuan Zhu, Alexander Xiong, Lakshya A Agrawal, Huanzhi Mao, Emma Shen, Sid Pallerla, Liana Patel 等

LLM-based agents already operate in production across many industries, yet we lack a clear understanding of which technical methods make these deployments successful. We present the first systematic study of Characterizing Agents in Production (CAP) using first-hand data from agent developers. We conducted 20 in-depth case studies through interviews and surveyed 306 practitioners across 26 domains. We examine why organizations build agents, how they build them, how they evaluate them, and the key challenges they face in deployment. Our findings show that production agents rely on simple, controllable approaches: 68% execute at most 10 steps before human intervention, 70% rely on prompting off-the-shelf models rather than weight tuning, and 74% depend primarily on human evaluation. Reliability—defined as consistent correct behavior over time—emerges as the dominant challenge, which practitioners address through system-level design choices. CAP documents the current state of production agents, providing the research community with visibility into real-world deployment practices and underexplored research opportunities.

Qixin Xiao, Kun Zhou

Video language models (Video-LLMs) are prone to hallucinations, often generating plausible but ungrounded content when visual evidence is weak, ambiguous, or biased. Existing decoding methods, such as contrastive decoding (CD), rely on random perturbations to construct contrastive data for mitigating hallucination patterns. However, such a way is hard to control the visual cues that drive hallucination or well align with model weaknesses. We propose Model-aware Counterfactual Data based Contrastive Decoding (MACD), a new inference strategy that combines model-guided counterfactual construction with decoding. Our approach uses the Video-LLM’s own feedback to identify object regions most responsible for hallucination, generating targeted counterfactual inputs at the object level rather than arbitrary frame or temporal modifications. These model-aware counterfactual data is then integrated into CD to enforce evidence-grounded token selection during decoding. Experiments on EventHallusion, MVBench, Perception-test and Video-MME show that MACD consistently reduces hallucination while maintaining or improving task accuracy across diverse Video-LLMs, including Qwen and InternVL families. The method is especially effective in challenging scenarios involving small, occluded, or co-occurring objects. Our code and data will be publicly released.

Applications · Computer Vision

Guojian Zhao, Huibing Wang, Jinjia Peng, Linfeng Qi, Mingze Yao, Jiqing Zhang

Unsupervised Domain Adaptation (UDA) person search aims to transfer a model trained on a labeled source domain to an unlabeled target domain without using target annotations. However, existing UDA methods frequently neglect the issue of scale inconsistency between the source and target domains. These inconsistency arises from different variations in Camera height, tilt angle, and focal length change. To address this challenge, we propose a Scale-Aware Consistent Alignment Learning (SCALE) framework. Specifically, we propose a Scale-aware Domain Harmonization (SDH) adaptively harmonizes semantic and structural scales through cross-path interaction and consistency refinement to alleviate cross-domain scale inconsistency. To further improve the pseudo-label inaccuracies, we introduce a Bidirectional Cluster Regularization (BCR) strategy, which obtains more reliable pseudo-labels by refining the results a second time. By collaboratively alleviating the impact of scale misalignment and enhancing pseudo-label reliability, our approach achieves state-of-the-art performance on two benchmark person search datasets, with 82.3% mAP and 84.0% top-1 on the CUHK-SYSU dataset, 41.7% mAP and 82.4% top-1 on the PRW dataset.

Applications · Neuroscience, Cognitive Science

Subba Reddy Oota, Satya Sai Srinath Namburi GNVV, Vijay Rowtula, Khushbu Pahwa, Anant Khandelwal, Manish Gupta, Tanmoy Chakraborty, Raju Bapi

Recent work has shown that scaling large language models (LLMs) improves their alignment with human brain activity, yet it remains unclear what drives these gains or which representational properties are responsible. Although larger models often yield better task performance and brain alignment, they are increasingly difficult to analyze mechanistically. This raises a fundamental question: \emph{what is the minimal model capacity required to capture brain-relevant representations?} To address this question, we systematically investigate how constraining model scale and numerical precision affects brain alignment. We compare full-precision LLMs, small language models (SLMs), and compressed variants (quantized and pruned) by predicting fMRI responses during naturalistic language comprehension. Across model families up to 14B parameters, we find that 3B SLMs achieve brain predictivity indistinguishable from larger LLMs, whereas 1B models degrade substantially, particularly in semantic language regions. Brain alignment is remarkably robust to compression: most quantization and pruning methods preserve neural predictivity, with GPTQ as a consistent exception. Linguistic probing reveals a dissociation between task performance and brain predictivity: compression degrades discourse, syntax, and morphology, yet brain predictivity remains largely unchanged. Overall, brain alignment saturates at modest model scales and is resilient to compression, challenging common assumptions about neural scaling and motivating compact models for brain-aligned language modeling.

General Machine Learning · Representation Learning

Weiling Li, Zhaoheng Shi, Jiajia Mi, Zhigang Liu, Jialiang Wang, Xin Luo

Latent Factorization-of-Tensors (LFT) models are an effective approach for relation extraction. Existing LFT models assume each mode of the target tensor corresponds to a entity set and the relationships between entity sets are bipartite graphs to explore the relationships among entities within a mode. However, when the topological structure of entities in a mode is known, for example, entities are ordered physical quantities, such as time or coordinates, the relation between such modes forms a more complicated system, i.e., aligned bipartite networks, and existing LFT models cannot accurately capture this structure. This work is the first to recognize and analyze this issue, and proposes an Asymmetric Latent Factorization-of-Tensors (ALFT) model to address it. ALFT can understand aligned bipartite networks in mode pairs of a tensor by imposing constraints between particular mode pairs in the tensor network. Experimental results on real-world datasets demonstrate the existence of this issue and confirm that the proposed ALFT model can effectively resolve it.