Daily TMLR digest for Feb 15, 2026
TMLR
Accepted papers
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Title: A simple connection from loss flatness to compressed neural representations
Authors: Shirui Chen, Stefano Recanatesi, Eric Todd SheaBrown
Abstract: Despite extensive study, the fundamental significance of sharpness---the trace of the loss Hessian at local minima---remains unclear. While often associated with generalization, recent work reveals inconsistencies in this relationship. We explore an alternative perspective by investigating how sharpness relates to the geometric structure of neural representations in feature space. Specifically, we build from earlier work by Ma and Ying to broadly study compression of representations, defined as the degree to which neural activations concentrate when inputs are locally perturbed. We introduce three quantitative measures: the Local Volumetric Ratio (LVR), which captures volume contraction through the network; the Maximum Local Sensitivity (MLS), which measures maximum output change normalized by the magnitude of input perturbations; and Local Dimensionality, which captures uniformity of compression across directions.
We derive upper bounds showing that LVR and MLS are mathematically constrained by sharpness: flatter minima necessarily limit these compression metrics. These bounds extend to reparametrization-invariant sharpness (measures unchanged under layer rescaling), addressing a key limitation of standard sharpness. We introduce network-wide variants (NMLS, NVR) that account for all layer weights, providing tighter and more stable bounds than prior single-layer analyses. Empirically, we validate these predictions across feedforward, convolutional, and transformer architectures, demonstrating consistent positive correlation between sharpness and compression metrics. Our results suggest that sharpness fundamentally quantifies representation compression rather than generalization directly, offering a resolution to contradictory findings on the sharpness-generalization relationship and establishing a principled mathematical link between parameter-space geometry and feature-space structure. Code is available at \url{https://github.com/chinsengi/sharpness-compression}.
URL: https://openreview.net/forum?id=GgpQbU9bFR
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Title: When Does LoRA Reuse Work? Theoretical Limits and Mechanisms for Recycling LoRAs Without Data Access
Authors: Mei-Yen Chen, Thi Thu Uyen Hoang, Michael Hahn, M. Saquib Sarfraz
Abstract: Reusing low-rank adapters (LoRAs) by merging or routing is a common strategy for adapting
large language models to new tasks, especially when training data is unavailable but many
fine-tuned LoRAs are accessible. While the availability of publicly shared LoRA weights has
inspired new algorithms for composing them to solve new tasks, recent findings highlight
limitations in LoRA’s ability to integrate new knowledge. This work investigates when LoRA
reuse can be successful for compositional factual and reasoning tasks. Through theoretical
analysis in a simplified setup and experiments on a controlled synthetic two-hop reasoning task
with extensions to math word problems, cross-lingual code generation, and history/geography
QA, we show that data-agnostic methods, such as parameter averaging and dynamic selection,
often fail to combine knowledge from logically disjoint fine-tuning datasets. This challenge is
particularly pronounced when the relevant knowledge is underrepresented during pretraining.
However, reuse can succeed when fine-tuning datasets share solution templates, such as
reasoning patterns or reusable code, which serve as bridges among tasks. Our results suggest
that LoRA reuse relies more on shallow pattern matching than on logical integration of
existing knowledge. This mechanism-based perspective offers practical guidance for curating
datasets and designing systems that enable LoRA reuse to overcome data-access limitations.
Findings indicate that future research should focus on the mechanisms enabling effective
adapter reuse rather than solely on developing new reuse algorithms.
URL: https://openreview.net/forum?id=lVqUJlsnRy
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New submissions
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Title: Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment
Abstract: In the realm of Text-attributed Graphs (TAGs), traditional graph neural networks (GNNs) often fall short due to the complex textual information associated with each node. Recent methods have improved node representations by leveraging large language models (LLMs)
to enhance node text features, but these approaches typically require extensive annotations or fine-tuning across all nodes, which is both time-consuming and costly. To overcome these challenges, we introduce GAGA, an efficient framework for TAG representation learning.
GAGA reduces annotation time and cost by focusing on annotating only representative nodes and edges. It constructs an annotation graph that captures the topological relationships among these annotations. Furthermore, GAGA employs a two-level alignment module to effectively integrate the annotation graph with the TAG, aligning their underlying structures. Experiments show that GAGA achieves classification accuracy on par with or surpassing state-of-the-art methods while requiring only 1% of the data to be annotated, demonstrating its high efficiency.
URL: https://openreview.net/forum?id=UBIPauyTYp
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Title: Joint Encoding of KV-Cache Blocks for Scalable LLM Serving
Abstract: Modern large language models (LLMs) drive interactive AI systems but are bottlenecked by the memory-heavy growth of key-value (KV) caches, which limits real-time throughput under concurrent loads. Existing KV-cache compression methods rely on rigid heuristics, disrupt tensor layouts, or require specialized compute, hindering scalability and deployment.
We propose joint encoding of KV-cache blocks, which fuses similar blocks across requests and input chunks into shared representations while preserving standard cache structure. This alleviates the KV-cache memory bottleneck, supporting high-concurrency serving without specialized hardware. Theoretically, we analyze the rate-distortion tradeoff of fused cache blocks under a Poisson process model. Empirically, our method achieves up to 4.38$\times$ KV-cache compression with negligible accuracy loss across diverse LLMs and benchmarks, outperforming recent structured and adaptive compression baselines. In real LLM serving, joint encoding improves the token throughput by $\sim$40\% on a single-machine vLLM benchmark, demonstrating substantial gains in inference throughput. Code is available at
\href{https://anonymous.4open.science/r/kv_joint_encoding-55B0/}{\nolinkurl{kv_joint_encoding-55B0}}.
URL: https://openreview.net/forum?id=xh7IfhHtDW
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Title: Unifying Understanding and Generation in Vision-Language Models: Advances, Challenges, and Opportunities
Abstract: Significant advancements in vision-language models have predominantly followed two divergent trajectories: autoregressive architectures optimized for visual understanding and diffusion-based frameworks designed for high-fidelity generation. However, this separation hinders the development of truly versatile multimodal agents. Unifying these capabilities is a critical step toward Artificial General Intelligence, as recent findings suggest that effective understanding and generation can mutually reinforce each other. This survey provides a comprehensive overview of the emerging field of unified vision-language models and proposes a systematic taxonomy based on the core visual representation mechanism: \textit{continuous} versus \textit{discrete} visual tokens. For continuous visual tokens, we analyze how models bridge the semantic-visual gap by categorizing integration strategies into Serial Coupling, where LLMs act as planners, and Parallel Coupling, which enables bidirectional interaction. regarding discrete visual tokens, we contrast Autoregressive approaches that treat images as a foreign language against emerging Discrete Diffusion paradigms known for their global consistency and parallel decoding. Beyond architectural analysis, we provide a curated compilation of datasets and benchmarks essential for training and evaluation. Finally, we critically discuss open challenges such as tokenization trade-offs, training stability, and scalability, while outlining future directions for building seamless, omni-capable multimodal systems.
URL: https://openreview.net/forum?id=AIMmeOrVFL
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Title: "COMPLEXITY-DEEP: A Language Model Architecture with Mu-Guided Attention and Token-Routed MLP"
Abstract: We present COMPLEXITY-DEEP, a language model (LLM) architecture developed from scratch, introducing three original contributions: (1) Token-Routed MLP, a dynamic per-token routing mechanism inspired by Mixture of Experts but without requiring auxiliary load balancing loss, (2) Mu-Guided Attention, where a latent state μ from the previous layer guides K, Q, and V projections, creating a bidirectional information flow between attention and dynamics, and (3) a PiD-style adaptive controller that stabilizes training through dynamic scaling. We provide formal theoretical analysis proving perfect load balance, capacity equivalence with dense models at 1/n compute cost, gradient-driven expert orthogonalization, and establish connections between Mu-Guidance and predictive coding theory. Our 1.5B parameter implementation, trained on 33B tokens from FineWeb-Edu, demonstrates the viability of this architecture with stable convergence (loss 3.78, perplexity 43.7). Evaluation on standard benchmarks shows performance consistent with model size, with supervised fine-tuning achieving 30% on MMLU (+5% above random) and 23% on ARC-Challenge.
URL: https://openreview.net/forum?id=jZq6EVboC6
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Title: AlignSAE: Concept-Aligned Sparse Autoencoders
Abstract: Large Language Models (LLMs) encode factual knowledge within hidden parametric spaces that are difficult to inspect or control. While Sparse Autoencoders (SAEs) can decompose hidden activations into more fine-grained, interpretable features, they often struggle to reliably align these features with human-defined concepts, resulting in entangled and distributed feature representations. To address this, we introduce AlignSAE, a method that aligns SAE features with a predefined ontology through a "pre-train, then post-train" curriculum. After an initial unsupervised training phase, we apply supervised post-training to bind specific concepts to dedicated latent slots while preserving the remaining capacity for general reconstruction. This separation creates an interpretable interface where specific concepts can be inspected and controlled without interference from unrelated features. Empirical results demonstrate that AlignSAE enables precise causal interventions, such as reliable "concept swaps", by targeting single, semantically aligned slots, and further supports multi-hop reasoning and a mechanistic probe of grokking-like generalization dynamics.
URL: https://openreview.net/forum?id=I9UjKxW4nq
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Title: Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights
Abstract: We examine the reasoning and planning capabilities of large language models (LLMs) in solving complex tasks. Recent advances in inference-time techniques demonstrate the potential to enhance LLM reasoning without additional training by exploring intermediate steps during inference. Notably, OpenAI's latest reasoning models show promising performance through use of multi-step reasoning and verification. Here, we explore how scaling inference-time techniques can improve reasoning and planning, focusing on understanding the tradeoff between computational cost and performance. To this end, we construct a comprehensive benchmark, known as *Sys2Bench*, and perform extensive experiments evaluating existing inference-time techniques on eleven diverse tasks across five categories, including arithmetic reasoning, logical reasoning, common sense reasoning, algorithmic reasoning, and planning.
*Sys2Bench* provides a unified framework for revealing the strengths and limitations of current inference-time methods, setting the stage for more principled and scalable approaches to LLM reasoning.
URL: https://openreview.net/forum?id=budZJyCK8G
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Title: VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use
Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated success in enhancing LLM reasoning capabilities, but remains limited to single-turn interactions without tool integration. While recent **A**gentic **R**einforcement **L**earning with **T**ool use (ARLT) approaches have emerged to address multi-turn tool interactions, existing works develop task-specific codebases that suffer from fragmentation, synchronous execution bottlenecks, and limited extensibility across domains. These inefficiencies hinder broader community adoption and algorithmic innovation. We introduce **VerlTool**, a unified and modular framework that addresses these limitations through systematic design principles. VerlTool provides four key contributions: **(1)** upstream alignment with VeRL ensuring compatibility and simplified maintenance, **(2)** unified tool management via standardized APIs supporting diverse modalities including code execution, search, SQL databases, and vision processing, **(3)** asynchronous rollout execution achieving near 2
speedup by eliminating synchronization bottlenecks, and **(4)** a comprehensive evaluation demonstrating competitive performance across 6 ARLT domains. Our framework formalizes ARLT as multi-turn trajectories with multi-modal observation tokens (text/image/video), extending beyond single-turn RLVR paradigms. We train and evaluate models on mathematical reasoning, knowledge QA, SQL generation, visual reasoning, web search, and software engineering tasks, achieving results comparable to specialized systems while providing a unified training infrastructure. The modular plugin architecture enables rapid tool integration requiring only lightweight Python definitions, significantly reducing development overhead and providing a scalable foundation for tool-augmented RL research.
URL: https://openreview.net/forum?id=g2LCOW43Md
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Title: Enhancing Interpretability: A Versatile Clue-Based Framework for Faithful In-Depth Interpretations
Abstract: Despite the state-of-the-art performance of deep neural networks, they are susceptible to bias and malfunction in unforeseen situations. Moreover, the complex computations underlying their reasoning are not human-understandable, hindering the development of trust and the validation of decisions. Local interpretation methods seek to provide explanations for individual model decisions with two key goals: faithfulness to the model and human-understandability. However, existing approaches often suffer from performance loss, limited applicability to pre-trained models, and unfaithful explanations. Seeking more faithful interpretations, we introduce a novel definition, called Distinguishing Clue, which sets of input regions that uniquely promote specific network decisions, detected through our Local Attention Perception (LAP) module. Our innovative training scheme allows LAP to learn these clues without relying on expert annotations. It also provides means for both general and expert knowledge injection. The system is usable for training networks from scratch, enhancing their interpretability, and interpreting networks that have already been trained. We demonstrate the superiority of the proposed method by evaluating it on different architectures across two datasets, including ImageNet. The proposed framework offers more valid and faithful-to-the-model interpretations than the commonly used explainer methods.
URL: https://openreview.net/forum?id=70STejAuwx
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Title: EMG-JEPA: Towards Scalable and Generalizable sEMG-Based Hand Pose Estimation via Self-Supervised Learning
Abstract: This work introduces EMG-JEPA, a Joint Embedding Predictive Architecture (JEPA) designed to improve generalization for hand pose estimation from surface electromyography (sEMG) signals. Collecting labeled sEMG data for hand pose estimation is costly, as it requires synchronizing the sEMG recordings with motion capture systems to obtain precise joint-angle annotations. To mitigate the dependency on such expensive labels, EMG-JEPA uses self-supervised learning to derive transferable representations from unlabeled sEMG signals, which can then be fine-tuned for downstream hand pose estimation. We analyze the effectiveness of EMG-JEPA on data collected from three wrist-worn devices, providing signals with 8, 16, and 110 channels. Our results show that EMG-JEPA can improve cross-user hand pose estimation, particularly in high-channel-density settings, reducing joint-angle error by up to 3.55% and 5.13% for the 16- and 110-channel setups, respectively. Further, results from the 8-channel setup suggest a channel-density threshold (≈16 channels), below which JEPA-based pretraining offers limited gains. Overall, our study identifies key design choices for developing a JEPA for sEMG, offering a scalable approach to reduce labeled data requirements.
URL: https://openreview.net/forum?id=H4PM2SsSor
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Title: Large Language Models as Interfaces to Structured Data: A Survey
Abstract: Structured data, including tables, relational databases, and knowledge graphs, underpins a wide range of scientific, industrial, and decision-making workflows. Although large language models (LLMs) are primarily trained on unstructured text, recent work has demonstrated their effectiveness in tasks involving structured data, such as table reasoning, natural language to SQL translation, data transformation, and automated analytics. These developments indicate that LLMs can function as a general interface between natural language inputs, structured representations, and executable operations.
This survey presents a theory-oriented overview of LLMs for structured data. We introduce an abstract formulation that characterizes structured data tasks by the structured state, the query or control input, the output space, and the execution environment. Based on this formulation, which we revisit throughout the taxonomy and evaluation sections, we propose a taxonomy that organizes existing methods according to the functional role of the LLM, including encoding, reasoning, translation, planning, and agent-based execution, as well as by representation strategies and learning signals. This taxonomy highlights shared design principles across different task settings and clarifies methodological trade-offs.
We examine evaluation protocols, generalization properties, and failure modes specific to structured data tasks, with an emphasis on faithfulness, schema robustness, and execution correctness. Finally, we outline open research directions for LLM-based structured data systems, including challenges related to scalability, symbolic and neural integration, and learning with execution-based supervision. The survey aims to provide a unified conceptual framework and a reference point for future research on large language models applied to structured data.
URL: https://openreview.net/forum?id=2z8fcjrN5Q
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Title: The LLM Data Auditor: A Metric-oriented Survey on Quality and Trustworthiness in Evaluating Synthetic Data
Abstract: Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities. By transforming data from a scarce resource into a controllable asset, LLMs mitigate the bottlenecks imposed by the acquisition costs of real-world data for model training, evaluation, and system iteration. However, ensuring the high quality of LLM-generated synthetic data remains a critical challenge. Existing research primarily focuses on generation methodologies, with limited direct attention to the quality of the resulting data. Furthermore, most studies are restricted to single modalities, lacking a unified perspective across different data types. To bridge this gap, we propose the LLM Data Auditor framework. In this framework, we first describe how LLMs are utilized to generate data across six distinct modalities. More importantly, we systematically categorize intrinsic metrics for evaluating synthetic data from two dimensions: quality and trustworthiness. This approach shifts the focus from extrinsic evaluation, which relies on downstream task performance, to the inherent properties of the data itself. Using this evaluation system, we analyze the experimental evaluations of representative generation methods for each modality and identify substantial deficiencies in current evaluation practices. Based on these findings, we offer concrete recommendations for the community to improve the evaluation of data generation. Finally, the framework outlines methodologies for the practical application of synthetic data across different modalities.
Our repository has been released: \href{https://anonymous.4open.science/r/Awesome-LLM-Data-Generation-6457/README.md}{https://anonymous.4open.science/r/Awesome-LLM-Data-Generation-6457}.
URL: https://openreview.net/forum?id=f2gS9Ly6tA
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Title: Improving OOD Robustness via Background-Aware Test- Time-AugmentationinBlack-BoxandResourceConstrained Settings
Abstract: Deep learning models for text classification typically achieve strong performance on in-distribution (ID) data but often fail to generalize to out-of-distribution (OOD) inputs. This degradation frequently arises because models rely on spurious background cues (e.g., specific syntax or register) learned during training, which become unreliable when the domain changes. While recent Test-Time Augmentation (TTA) approaches have enabled robustness in black-box settings, they often rely on unconstrained rewriting strategies. For instance, standard In-Context Rewriting (ICR) instructs Large Language Models (LLMs) to modify input details to match ID exemplars, creating a high risk of semantic drift and label flipping, particularly when using smaller, resource-constrained LLMs. In this work, we propose a Background-Aware TTA framework that strictly disentangles style from semantics. Unlike prior methods that encourage broad paraphrasing, we utilize a semantic-constrained alignment strategy that enables small, efficient LLMs to transform specific background attributes, such as tone and sentence structure, to match in-distribution priors while explicitly enforcing the preservation of original meaning. This approach mitigates OOD degradation by neutralizing spurious background shifts, allowing frozen black-box models to process inputs in their native distribution without risking semantic corruption. Empirical evaluations across multiple text classification benchmarks demonstrate that our targeted alignment strategy outperforms unconstrained augmentation baselines. By generating higher-fidelity augmentations, our method achieves superior OOD robustness with reduced computational overhead, establishing a viable path for deploying robust in resource-limited black-box environments.
URL: https://openreview.net/forum?id=xptPQVCy5X
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Title: Iterative Preference Optimization with Proximal Policy Regularization for Large Language Model Alignment
Abstract: Aligning large language models (LLMs) with human preferences is commonly achieved via supervised fine-tuning followed by preference optimization. While direct preference optimization (DPO) offers a simple and efficient alternative to RLHF, its offline and off-policy nature can induce a distribution shift between the policy used to sample preference pairs and the continually updated policy being optimized, reducing data efficiency and limiting alignment gains. We propose \emph{Iterative Proximal Policy Regularized Preference Optimization} (Iterative PRPO), which introduces a proximal regularization that explicitly constrains the optimized policy to remain close to the sampling policy within each iteration, thereby mitigating distribution shift while preserving the efficiency of DPO-style updates. Starting from an RLHF objective with a KL constraint to the sampling policy, we derive an equivalent direct preference optimization formulation that requires offline preference pairs under the sampling policy. Across summarization and dialogue alignment benchmarks, Iterative PRPO consistently improves win rates over offline DPO and iterative DPO baselines under both reward-model and GPT-4o evaluations, with comparable computational cost. Moreover, the same proximal regularization principle generalizes to advanced preference optimization objectives, including Identity Preference Optimization (IPO), self-play preference optimization (SPPO), and efficient exact optimization (EXO), yielding Iterative PR-IPO, PR-SPPO, and PR-EXO variants that further strengthen alignment across model scales.
URL: https://openreview.net/forum?id=xoxO5Tr4Vh
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