Daily TMLR digest for Feb 21, 2026
TMLR
Accepted papers
===============
Title: Reproducibility Study: Understanding multi-agent LLM cooperation in the GovSim framework
Authors: Alessio Silverio, Carmen Michaela Chezan, Mathijs van Sprang, Tom Cappendijk, Martin Smit
Abstract: Governance of the Commons Simulation (GovSim) is a Large Language Model (LLM) multi-agent framework designed to study cooperation and sustainability between LLM agents in resource-sharing environments (Piatti et al., 2024). Understanding the cooperation capabilities of LLMs is vital to the real-world applicability of these models. This study reproduces and extends the original GovSim experiments using recent small-scale open-source LLMs, including newly released instruction-tuned models such as Phi-4 and DeepSeek-R1 distill variants. We evaluate three core claims from the original paper: (1) GovSim enables the study and benchmarking of emergent sustainable behavior, (2) only the largest and most powerful LLM agents achieve a sustainable equilibrium, while smaller models fail, and (3) agents using universalization-based reasoning significantly improve sustainability. Our findings support the first claim, demonstrating that GovSim remains a valid platform for studying social reasoning in multi-agent LLM systems. However, our results challenge the second claim: recent smaller-sized LLMs, particularly DeepSeek-R1-Distill-Qwen-14B, achieve sustainable equilibrium, indicating that advancements in model design and instruction tuning have narrowed the performance gap with larger models. Regarding the third claim, our results confirm that universalization-based reasoning improves performance in the GovSim environment, supporting the third claim of the author. However, further analysis suggests that the improved performance primarily stems from the numerical instructions provided to agents rather than the principle of universalization itself. To further generalize these findings, we extended the framework to include a broader set of social reasoning frameworks. We find that reasoning strategies incorporating explicit numerical guidance consistently outperform abstract ethical prompts, highlighting the critical role of prompt specificity in influencing agent behavior.
URL: https://openreview.net/forum?id=ON8EMrNwww
---
Title: A Multilevel Low-Rank Newton Method with Super-linear Convergence Rate and its Application to Non-convex Problems
Authors: Nick Tsipinakis, Panagiotis Tigas, Panos Parpas
Abstract: Second-order methods can address the shortcomings of first-order methods for the optimization of large-scale machine learning models.
However, second-order methods have significantly higher computational costs associated with the computation of second-order information. Subspace methods that are based on randomization have addressed some of these computational costs as they compute search directions in lower dimensions. Even though super-linear convergence rates have been empirically observed, it has not been possible to rigorously show that these variants of second-order methods can indeed achieve such fast rates.
Also, it is not clear whether subspace methods are efficient for non-convex settings.
To address these shortcomings, we develop a link between multigrid optimization methods and low-rank Newton methods that enables us to prove the super-linear rates of stochastic low-rank Newton methods rigorously. Our method does not require any computations in the original model dimension. We further propose a truncated version of the method that is capable of solving high-dimensional non-convex problems. Preliminary numerical experiments show that our method has a better escape rate from saddle points compared to the state-of-the-art first-order methods and thus returns lower training errors.
URL: https://openreview.net/forum?id=PKakPzVVja
---
Title: From Preferences to Prejudice: The Role of Alignment Tuning in Shaping Social Bias in Video Diffusion Models
Authors: Zefan Cai, Haoyi Qiu, Haozhe Zhao, Ke Wan, Jiachen Li, Jiuxiang Gu, Wen Xiao, Nanyun Peng, Junjie Hu
Abstract: Recent advances in video diffusion models have significantly enhanced text-to-video generation, particularly through alignment tuning using reward models trained on human preferences. While these methods improve visual quality, they can unintentionally encode and amplify social biases. To systematically trace how such biases evolve throughout the alignment pipeline, we introduce VideoBiasEval, a comprehensive diagnostic framework for evaluating social representation in video generation. Grounded in established social bias taxonomies, VideoBiasEval employs an event-based prompting strategy to disentangle semantic content (verbs and contexts) from actor attributes (gender and ethnicity). It further introduces multi-granular metrics to evaluate (1) overall ethnicity bias, (2) gender bias conditioned on ethnicity, (3) distributional shifts in social attributes across model variants, and (4) the temporal persistence of bias within videos. Using this framework, we conduct the first end-to-end analysis connecting biases in human preference datasets, their amplification in reward models, and their propagation through alignment-tuned video diffusion models. Our results reveal that alignment tuning not only strengthens representational biases but also makes them temporally stable, producing smoother yet more stereotyped portrayals. These findings highlight the need for bias-aware evaluation and mitigation throughout the alignment process to ensure fair and socially responsible video generation.
URL: https://openreview.net/forum?id=C0yxuS6jty
---
Title: Semi-Supervised Cross-Domain Imitation Learning
Authors: Li-Min Chu, Kai-Siang Ma, Ming-Hong Chen, Ping-Chun Hsieh
Abstract: Cross-domain imitation learning (CDIL) accelerates policy learning by transferring expert knowledge across domains, which is valuable in applications where collection of expert data is costly. Existing methods are either supervised, relying on proxy tasks and explicit alignment, or unsupervised, aligning distributions without paired data but often unstable. We introduce the Semi-Supervised CDIL (SS-CDIL) setting and propose the first algorithm for SS-CDIL with theoretical justification. Our method uses only offline data, including a small number of target expert demonstrations and some unlabeled imperfect trajectories. To handle domain discrepancy, we propose a novel cross-domain loss function for learning inter-domain state-action mappings and design an adaptive weight function to balance the source and target knowledge. Experiments on MuJoCo and Robosuite show consistent gains over the baselines, demonstrating that our approach achieves stable and data-efficient policy learning with minimal supervision.
URL: https://openreview.net/forum?id=WARXnbJawZ
---
Title: Exploring Perceptual Limitations of Multimodal LLMs on Small Visual Objects
Authors: Jiarui Zhang, Jinyi Hu, Mahyar Khayatkhoei, Filip Ilievski, Maosong Sun
Abstract: Multimodal Large Language Models (MLLMs) have recently achieved remarkable performance in various multimodal benchmarks. However, general benchmarks often do not reveal the specific aspects of their visual perception limits due to the lack of controllability. In this work, we quantitatively study the perception of small visual objects in several widely-used MLLMs and reveal a pervasive limitation in answering questions about small objects in images. We then conduct a controlled study of MLLMs' perception, using text-reading as a surrogate task for general visual perception to understand how quality, size, distractors, and location of an object can independently affect the ability of MLLMs to perceive it in images. Through this controlled study, we find that lower object quality, smaller object size and the presence of visual distractors can both independently reduce MLLMs' ability to answer visual questions. More surprisingly, even local perturbations of an object by a few pixels can cause a drastic decline in the ability of MLLMs to perceive it. Our study provides a better understanding of the perceptual limitations of MLLMs and contributes new evaluation protocols for analyzing, enhancing perception of future MLLMs.
URL: https://openreview.net/forum?id=D8MjYW8m35
---
New submissions
===============
Title: Likelihood-based Fine-tuning of Protein Language Models for Few-shot Fitness Prediction and Design
Abstract: Machine learning models trained on measurements of protein functional properties are widely used to accelerate laboratory-based protein design campaigns. To maximise the signal that can be extracted from limited experimental data, sequence embeddings produced by protein language models (PLMs) are often used as the basis of supervised fitness predictors. However, embedding-based predictors do not directly exploit the distributional information encoded in PLM likelihoods after self-supervised or generative pretraining on natural protein sequences. In contrast, likelihood-based fine-tuning approaches exploit this prior knowledge by directly updating pretrained PLM likelihoods to reflect observed fitness differences between sequences. While likelihood-based fine-tuning methods have been proposed previously, a conclusive comparison of their performance against state-of-the-art embedding-based methods has been lacking. To address this gap, we conduct a comprehensive empirical evaluation of both fine-tuning strategies on a representative set of protein fitness datasets from the ProteinGym benchmark. To ensure our evaluation is applicable across different PLM classes, we develop a simple, unified framework for likelihood-based fine-tuning that applies to models trained with various objectives. Across model classes and fitness datasets, likelihood-based fine-tuning consistently outperforms embedding-based methods previously reported as state-of-the-art, with the largest gains in low-data settings. Finally, to highlight the practical relevance of these findings, we demonstrate that the best-performing fine-tuning strategies can substantially improve the maximal fitness of designed sequences in multi-round in silico optimisation campaigns.
URL: https://openreview.net/forum?id=vfTcUT220j
---
Title: RPWithPrior: Label Differential Privacy in Regression
Abstract: With the wide application of machine learning techniques in practice, privacy preservation has gained increasing attention. Protecting user privacy with minimal accuracy loss is a fundamental task in the data analysis and mining community. In this paper, we focus on regression tasks under $\epsilon$-label differential privacy guarantees. Some existing methods for regression with $\epsilon$-label differential privacy, such as the RR-On-Bins mechanism, discretized the output space into finite bins and then applied RR algorithm. To efficiently determine these finite bins, the authors rounded the original responses down to integer values. However, such operations does not align well with real-world scenarios. To overcome these limitations, we model both original and randomized responses as continuous random variables, avoiding discretization entirely. Our novel approach estimates an optimal interval for randomized responses and introduces new algorithms designed for scenarios where a prior is either known or unknown. Additionally, we prove that our algorithm, RPWithPrior, guarantees $\epsilon$-label differential privacy and provide error analysis. Numerical results demonstrate that our approach gets better performance compared with the Gaussian, Laplace, Staircase, and RRonBins, Unbiased mechanisms on the Communities and Crime, Criteo Sponsored Search Conversion Log, California Housing datasets.
URL: https://openreview.net/forum?id=FiUe0OCMaj
---
Title: CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimization
Abstract: Heatmap-based solvers have emerged as a promising paradigm for Combinatorial Optimization (CO). However, we argue that the dominant Supervised Learning (SL) training paradigm suffers from a fundamental objective mismatch: minimizing imitation loss (e.g., cross-entropy) does not guarantee solution cost minimization. We dissect this mismatch into two deficiencies: Decoder-Blindness (being oblivious to the non-differentiable decoding process) and Cost-Blindness (prioritizing structural imitation over solution quality). We empirically demonstrate that these intrinsic flaws impose a hard performance ceiling. To overcome this limitation, we propose CADO (Cost-Aware Diffusion models for Optimization), a streamlined Reinforcement Learning fine-tuning framework that formulates the diffusion denoising process as an MDP to directly optimize the post-decoded solution cost. We introduce Label-Centered Reward, which repurposes ground-truth labels as unbiased baselines rather than imitation targets, and Hybrid Fine-Tuning for parameter-efficient adaptation. CADO achieves state-of-the-art performance across diverse benchmarks, validating that objective alignment is essential for unlocking the full potential of heatmap-based solvers.
URL: https://openreview.net/forum?id=fvxx5FOED6
---
Title: Sequential Causal Discovery with Noisy Language Model Priors
Abstract: Causal discovery from observational data typically assumes access to complete data and availability of domain experts. In practice, data often arrive in batches, are subject to sampling bias, and expert knowledge is scarce. Language Models (LMs) offer a surrogate for expert knowledge but suffer from hallucinations, inconsistencies, and bias. We present a hybrid framework that bridges these gaps by adaptively integrating sequential batch data with LM-derived noisy, expert knowledge while accounting for both data-induced and LM-induced biases. We propose a representation shift from Directed Acyclic Graph (DAG) to Partial Ancestral Graph (PAG), that accommodates ambiguities within a coherent framework, allowing grounding the global LM knowledge in local observational data. To guide LM interactions, we use a sequential optimization scheme that adaptively queries the most informative edges. Across varied datasets and LMs, we outperform prior work in structural accuracy and extend to parameter estimation, showing robustness to LM noise.
URL: https://openreview.net/forum?id=wFs71JzEO7
---
Title: Hardware Acceleration for Neural Networks: A Comprehensive Survey
Abstract: Neural networks have become a dominant computational workload across cloud and edge platforms, but their rapid growth in model size and deployment diversity has exposed hardware bottlenecks that are increasingly dominated by memory movement, communication, and irregular operators rather than peak arithmetic throughput. This survey reviews the current technology landscape for hardware acceleration of deep learning, spanning Graphics Processing Units (GPUs) and tensor-core architectures, domain-specific accelerators (e.g., Tensor Processing Units (TPUs)/Neural Processing Units (NPUs)), Field-Programmable Gate Array (FPGA)-based designs, Application-Specific Integrated Circuit (ASIC) inference engines, and emerging Large Language Model (LLM)-serving accelerators such as Language Processing Units (LPUs), alongside in-/near-memory computing and neuromorphic/analog approaches. We organize the survey using a unified taxonomy across (i) workloads (Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), Transformers/Large Language Models (LLMs)), (ii) execution settings (training vs.\ inference; datacenter vs.\ edge), and (iii) optimization levers (reduced precision, sparsity and pruning, operator fusion, compilation and scheduling, and memory-system/interconnect design). We synthesize key architectural ideas such as systolic arrays, vector and Single Instruction, Multiple Data (SIMD) engines, specialized attention and softmax kernels, quantization-aware datapaths, and high-bandwidth memory, and we discuss how software stacks and compilers bridge model semantics to hardware. Finally, we highlight open challenges—including efficient long-context LLM inference (Key-Value (KV)-cache management), robust support for dynamic and sparse workloads, energy- and security-aware deployment, and fair benchmarking—pointing to promising directions for the next generation of neural acceleration.
URL: https://openreview.net/forum?id=Da8LO5NvDU
---
Title: Process Reinforcement through Implicit Rewards
Abstract: Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in tasks requiring complex multi-step reasoning. While dense rewards also offer an appealing choice for the reinforcement learning (RL) of LLMs since their fine-grained rewards have the potential to address some inherent issues of outcome rewards, such as training efficiency and credit assignment, this potential remains largely unrealized. This can be primarily attributed to the challenges of training process reward models (PRMs) online, where collecting high-quality process labels is prohibitively expensive, making them particularly vulnerable to reward hacking. To address these challenges, we propose PRIME (\underline{\textbf{P}}rocess \underline{\textbf{R}}einforcement through \underline{\textbf{IM}}plicit r\underline{\textbf{E}}wards), which enables online PRM updates using only policy rollouts and outcome labels through \textit{implicit process rewards}. PRIME combines well with various advantage functions and forgoes the dedicated reward model training phase that existing approaches require, substantially reducing the development overhead. We demonstrate PRIME's effectiveness on competitional math and coding. Starting from Qwen2.5-Math-7B-Base, PRIME achieves a 15.1\% average improvement across several key reasoning benchmarks over the SFT model. Notably, our resulting model, Eurus-2-7B-PRIME, surpasses Qwen2.5-Math-7B-Instruct on seven reasoning benchmarks with 10\% of its training data.
URL: https://openreview.net/forum?id=9SkkifLopZ
---
Title: Legal Alignment for Safe and Ethical AI
Abstract: Alignment of artificial intelligence (AI) encompasses the normative problem of specifying how AI systems should act and the technical problem of ensuring AI systems comply with those specifications. To date, AI alignment has generally overlooked an important source of knowledge and practice for grappling with these problems: law. In this paper, we aim to fill this gap by exploring how legal rules, principles, and methods can be leveraged to address problems of alignment and inform the design of AI systems that operate safely and ethically. This emerging field -- legal alignment -- focuses on three research directions: (1) designing AI systems to comply with the content of legal rules developed through legitimate institutions and processes, (2) adapting methods from legal interpretation to guide how AI systems reason and make decisions, and (3) harnessing legal concepts as a structural blueprint for confronting challenges of reliability, trust, and cooperation in AI systems. These research directions present new conceptual, empirical, and institutional questions, which include examining the specific set of laws that particular AI systems should follow, creating evaluations to assess their legal compliance in real-world settings, and developing governance frameworks to support the implementation of legal alignment in practice. Tackling these questions requires expertise across law, computer science, and other disciplines, offering these communities the opportunity to collaborate in designing AI for the better.
URL: https://openreview.net/forum?id=BypXEQa7mf
---
Title: White-Box Sensitivity Auditing with Steering Vectors
Abstract: Algorithmic audits are essential tools for examining systems for properties required by regulators or desired by operators. Current audits of large language models (LLMs) primarily rely on black-box evaluations that assess model behavior only through input–output testing. These methods are limited to tests constructed in the input space, often generated by heuristics. In addition, many socially relevant model properties (e.g., gender bias) are abstract and difficult to measure through text-based inputs alone. To address these limitations, we propose a white-box sensitivity auditing framework for LLMs that leverages activation steering to conduct more rigorous assessments through model internals. Our auditing method conducts internal sensitivity tests by manipulating key concepts relevant to the model's intended function for the task. We demonstrate its application to bias audits in four simulated high-stakes LLM decision tasks. Our method consistently reveals substantial dependence on protected attributes in model predictions, even in settings where standard black-box evaluations suggest little or no bias.
URL: https://openreview.net/forum?id=EfinGGyQRz
---
Title: Molecule Meets Protein Pocket 3D-Aware Molecular Optimization for Protein Targets
Abstract: Lead optimization, refining drug candidates to improve binding to protein targets, is a key challenge in drug discovery. We introduce a 3D-aware generative framework that performs fragment-level molecular optimization conditioned on the geometry of the protein's binding pocket. Our model represents the molecule-protein complex as a sparse 3D graph and applies grouped vector attention to learn spatial interactions. It decomposes the molecule into a stable scaffold and generates new fragments using a Variational Autoencoder (VAE) and a SMILES-based transformer guided by local pocket structure. To handle the imbalance in fragment sizes, we incorporate a focal loss. On the CrossDock2020 benchmark, our method outperforms prior approaches in generating diverse, novel, and chemically valid candidates with improved Vina scores-while generalizing to unseen proteins.
URL: https://openreview.net/forum?id=0irSt7bUGw
---
Title: Foundations and Frontiers of Multimodal Agentic Frameworks
Abstract: Advances in large language models (LLMs) have fueled a wave of research into agency: the ability to reason, plan, and act. This effort has produced agentic frameworks that orchestrate perception, memory, and decision-making around powerful LLM backbones. With the advent of large multimodal models (LMMs), these systems can process and integrate diverse modalities, including images, audio, and video, thereby improving their real-world applicability. Yet, while surveys of LLM-based agents exist, the role of multimodality in shaping agency has not been systematically examined in recent years. This survey fills the gap by analyzing the impact of multimodality across the core functional modules of the agentic framework: perception, reasoning, planning, memory, and action. Using this lens, we trace the evolution from text-centric agents to multimodal frameworks, examine how modalities are integrated through delegated, late-fusion, and early-fusion architectures, and assess the emergence of agentic behaviors enabled by grounded perception and multimodal reasoning. We organize existing work through a modality-centric taxonomy that links architectural design choices to agent capabilities. Moreover, we review multimodal agentic systems across various application domains, including Robotics, GUI & Web Navigation, Multimedia Content Generation & Editing, and Long-form Video Understanding & Retrieval. Beyond capabilities, we analyze performance across these settings and discuss efficiency-scalability trade-offs, including training and inference costs, latency, and deployment constraints. By focusing on the impact of multimodality in agentic design, we aim to identify key gaps and chart a roadmap toward robust and general-purpose intelligent systems.
URL: https://openreview.net/forum?id=eaVoaI7f8v
---
Title: Multi-Level Spatial Embedding Sharing for Enhanced Online Trajectory-User Linking
Abstract: Trajectory-User Linking (TUL) is a critical task in mobility applications that links unlabeled spatial trajectories to the users or entities that generated them. In these applications, data often arrives as a continuous stream and may experience distributional shifts over time. While adapting TUL models via online learning could address these challenges, this approach remains unexplored in current research. Our work bridges this gap by conducting comprehensive evaluations of common TUL techniques in an online learning context. To improve the performance of existing TUL techniques in this setting, we further introduce a novel embedding approach called Multi-Level Spatial Embedding Sharing (MiLES). MiLES operates by partially sharing embeddings for locations within neighborhoods of multiple size levels. This design enables faster adaptation via frequently-updated shared embeddings, while maintaining fine-grained discrimination through more location-specific representations. MiLES also significantly reduces the number of embedding parameters leading to lower memory usage and more computationally efficient model updates. We further incorporate learnable weighting parameters for each embedding level, allowing the model to dynamically adjust the influence of different levels based on incoming data. Our experimental results on several real-world datasets show that integrating MiLES into state-of-the-art TUL models significantly improves their performance in online learning scenarios, yielding relative gains in top-1 accuracy of up to 24%. To demonstrate its general applicability, we also evaluate MiLES on the task of destination prediction, where it also provides consistent performance improvements, confirming its value as a domain-general embedding technique. Our code is available at \url{https://anonymous.4open.science/r/MiLES-3D20}.
URL: https://openreview.net/forum?id=LGflWbxAuP
---
Title: MatchEx: Model-Level GNN Explanations with Multi-Granular Insights
Abstract: Graph Neural Networks (GNNs) are increasingly deployed in high-stakes domains where interpretability is crucial. Existing model-level explanation methods largely rely on generative models, which often produce motifs that fail to resemble real instances, cannot account for the diversity of discriminative motifs recognized by the classifier for a target class and lack mechanisms for translating global explanations to instance-level insights. We present MatchEx, a framework that discovers discriminative motifs directly from real instances by optimizing a novel matching objective. Unlike isomorphism, which can only recover identical motifs that rarely occur in real-world graphs, this objective extends beyond exact matches to provably recover semantically similar motifs, allowing generalizable explanations. The matching mechanism also enables projection of class level rationales onto individual graphs for faithful instance-level insights. When a single motif fails to explain all instances, MatchEx adaptively partitions the instances in a class into coherent subgroups with distinct rationales. Extensive experiments across six real and synthetic datasets show that MatchEx consistently outperforms state-of-the-art baselines, delivering coherent, generalizable, and multi-granular explanations.
URL: https://openreview.net/forum?id=YMETLG2WvM
---