Daily TMLR digest for Dec 30, 2025
TMLR
Accepted papers
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Title: Policy-Guided Search on Tree-of-Thoughts for Efficient Problem Solving with Bounded Language Model Queries
Authors: Sumedh Pendurkar, Guni Sharon
Abstract: Recent studies explored integrating state-space search algorithms with Language Models (LM) to perform look-ahead on the token generation process, the ``Tree-of-Thoughts'' (ToT), generated by LMs, thereby improving performance on problem-solving tasks. However, the affiliated search algorithms often overlook the significant computational costs associated with LM inference, particularly in scenarios with constrained computational budgets. Consequently, we address the problem of improving LM performance on problem-solving tasks under limited computational budgets. We demonstrate how the probabilities assigned to thoughts by LMs can serve as a heuristic to guide search within the ToT framework, thereby reducing the number of thought evaluations. Building on this insight, we adapt a heuristic search algorithm, Levin Tree Search (LTS), to the ToT framework, which leverages LMs as policies to guide the tree exploration efficiently. We extend the theoretical results of LTS by showing that, for ToT (a pruned tree), LTS guarantees a bound on the number of states expanded, and consequently, on the number of thoughts generated. Additionally, we analyze the sensitivity of this bound to the temperature values commonly used in the final softmax layer of the LM. Empirical evaluation under a fixed LM query budget demonstrates that LTS consistently achieves comparable or higher accuracy than baseline search algorithms within the ToT framework, across three domains (Blocksworld, PrOntoQA, Array Sorting) and four distinct LMs. These findings highlight the efficacy of LTS on ToT, particularly in enabling cost-effective and time-efficient problem-solving, making it well-suited for latency-critical and resource-constrained applications.
URL: https://openreview.net/forum?id=Rlk1bWe2ii
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Title: Universal Differential Equations for Stable Multi-Step Volatility Time Series Forecasting
Authors: Prasanna Devadiga, Kishan Gurumurthy, Kshitij Mohan
Abstract: Neural differential equations such as Neural ODEs, Neural CDEs, and Universal Differential Equations (UDEs) model temporal evolution as a continuous-time flow rather than a fixed-step recurrence. Even for regularly sampled data, this formulation differs fundamentally from discrete-time architectures: it learns smooth vector fields governing instantaneous rates of change, reducing discretization bias and improving long-horizon stability. We present a systematic study of Universal Differential Equations for financial volatility forecasting, a domain characterized by regime shifts, heavy tails, and jump discontinuities. UDEs extend Neural ODEs by embedding mechanistic structure within learned dynamics, using neural networks to parameterize coefficients in partially known differential equations instead of learning the system purely from data. Our UDE variants incorporate volatility’s empirical regularities while retaining neural flexibility for regime adaptation. Across market regimes, they outperform both continuous-time baselines and discrete-time models, achieving higher accuracy and greater long-horizon stability while remaining interpretable. These results suggest that UDEs grounded in mechanistic structure and neural flexibility offer a principled route to stable, interpretable multi-step forecasting in nonstationary domains.
URL: https://openreview.net/forum?id=uWGNexco2M
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Title: Occam’s Razor for SSL: Memory-Efficient Parametric Instance Discrimination
Authors: Eric Gan, Patrik Reizinger, Alice Bizeul, Attila Juhos, Mark Ibrahim, Randall Balestriero, David Klindt, Wieland Brendel, Baharan Mirzasoleiman
Abstract: Self-supervised learning (SSL) is the prevalent paradigm for representation learning often relying on pairwise similarity between multiple augmented views of each example. Numerous learning methods with various complexities such as gradient stopping, negative sampling, projectors, additional regularization terms, were introduced in the past years. These methods can be effective, but they require careful hyperparameter tuning, have increased computational and memory requirements and struggle with latent dimensionality collapse. Furthermore, complexities such as gradient stopping make them hard to analyse theoretically and confound the essential components of SSL. We introduce a simple parametric instance discrimination method, called Datum IndEx as its Target (DIET). DIET has a single computational branch, without explicit negative sampling, gradient stopping or other hyperparameters. We empirically demonstrate that DIET (1) can be implemented in a memory-efficient way; (2) achieves competitive performance with state-of-the-art SSL methods on small-scale datasets; and (3) is robust to hyperparameters such as batch size. We uncover tight connections to Spectral Contrastive Learning in the lazy training regime, leading to practical insights about the role of feature normalization. Compared to SimCLR or VICReg, DIET also has higher-rank embeddings on CIFAR100 and TinyImageNet, suggesting that DIET captures more latent information.
URL: https://openreview.net/forum?id=GFNTbsVFlP
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Title: Quantifying Context Bias in Domain Adaptation for Object Detection
Authors: Hojun Son, Asma A. Almutairi, Arpan Kusari
Abstract: Domain adaptation for object detection (DAOD) has become essential to counter performance degradation caused by distribution shifts between training and deployment domains. However, a critical factor influencing DAOD—context bias resulting from learned foreground-background (FG–BG) association—remains underexplored. In this work, we present the first comprehensive empirical and causal analysis specifically targeting context bias in DAOD. We address three key questions regarding FG–BG association in object detection: (a) whether FG–BG association is encoded during training, (b) whether there is a causal relationship between FG–BG association and detection performance, and (c) whether FG–BG association affects DAOD. To examine how models capture FG–BG association, we analyze class-wise and feature-wise performance degradation using background masking and feature perturbation, measured via change in accuracy (defined as drop rate). To explore the causal role of FG–BG association, we apply do-calculus to FG–BG pairs guided by class activation mapping (CAM). To quantify the causal influence of FG–BG association across domains, we propose a novel metric—Domain Association Gradient—defined as the ratio of drop rate to maximum mean discrepancy (MMD). Through systematic experiments involving background masking, feature-level perturbations, and CAM, we reveal that convolution-based object detection models encode FG–BG association. The association substantially impacts detection performance, particularly under domain shifts where background information significantly diverges. Our results demonstrate that context bias not only exists but also causally undermines the generalization capabilities of object detection models across domains. Furthermore, we validate these findings across multiple models and datasets, including state-of-the-art architectures such as ALDI++. This study highlights the necessity of addressing context bias explicitly in DAOD frameworks, providing insights that pave the way for developing more robust and generalizable object detection systems.
URL: https://openreview.net/forum?id=YRU0A0nraG
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Title: Exploring exploration with foundation agents in interactive environments
Authors: Daniel P. Sawyer, Nan Rosemary Ke, Hubert Soyer, Martin Engelcke, John Reid, David P Reichert, Drew A. Hudson, Alexander Lerchner, Danilo Jimenez Rezende, Timothy P Lillicrap, Michael Curtis Mozer, Jane X Wang
Abstract: Foundation models excel at single-turn reasoning, but many real-world challenges, from scientific research to technology development, require multi-turn exploration in dynamic interactive environments. Crucial components of learning from experience in these settings, such as efficiently gathering information to test hypotheses, meta-learning a model of the world's dynamics, and adapting to unexpected changes, remain largely unexplored for these models. We first evaluate foundation models in Feature World, a setting that primarily tests information gathering about a static hidden reward function. In this initial setting, we show that state-of-the-art foundation models come close to optimal efficiency in selecting maximally informative actions in tasks with simple reward functions. As a proof of concept, we also show a model can gather information efficiently in a 3D embodied version of this task, though errors in vision limit some aspects of performance. In order to test exploration across multiple dependent turns and trials, we implement a custom, text-based version of the Alchemy environment, a benchmark designed for meta-learning. Here, agents must deduce a latent causal structure by integrating information across multiple state-dependent trials. In this more complex setting, we find that recent foundation models struggle to meta-learn strategies that enable improved performance over time. However, prompting the models to summarize their observations at regular intervals enables an emergent meta-learning process, allowing them to improve across trials. Notably, in some models, summarization also enabled adaptive re-learning of this information when the environment's rules change unexpectedly. While most models performed reasonably well on simple Feature World tasks, evaluations in Alchemy reveal stark differences in robustness among the models, with Gemini 2.5 performing best, followed by Claude 3.7, and ChatGPT-4o and o4-mini struggling the most. These results underscore Alchemy's value as a benchmark for meta-learning and strategy adaptation in foundation models. By moving beyond simple discovery to complex, stateful environments, we demonstrate that the most significant challenge for foundation agents is not selecting informative actions in the moment, but rather seeking and integrating knowledge through adaptive strategies over time. Intriguingly, we find there is likely no intrinsic barrier to future generations of foundation agents more fully mastering these abilities.
URL: https://openreview.net/forum?id=wOrkUTr0W5
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New submissions
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Title: The Gaussian-Multinoulli Restricted Boltzmann Machine: A Potts Model Extension of the GRBM
Abstract: Many real-world tasks, from associative memory to symbolic reasoning, benefit from discrete, structured representations that standard continuous latent models can struggle to express. We introduce the Gaussian–Multinoulli Restricted Boltzmann Machine (GM-RBM), a generative energy-based model that extends the Gaussian–Bernoulli RBM (GB-RBM) by replacing binary hidden units with $q$-state categorical (Potts) units, yielding a richer latent state space for multivalued concepts. We provide a self-contained derivation of the energy, conditional distributions, and learning rules, and detail practical training choices (contrastive divergence with temperature annealing and intra-slot diversity constraints) that avoid state collapse. To separate architectural effects from sheer latent capacity, we evaluate under both capacity-matched and parameter-matched setups, comparing GM-RBM with GB-RBM configured to have the same number of possible latent assignments. On analogical recall and structured memory benchmarks, GM-RBM achieves competitive, and in several regimes, improved recall at equal capacity with comparable training cost, despite using only Gibbs updates. The discrete $q$-ary formulation is also amenable to efficient implementation. These results clarify when categorical hidden units provide a simple, scalable alternative to binary latents for discrete inference within tractable RBMs.
URL: https://openreview.net/forum?id=3QuXwfMcKo
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