论文检索

Optimization · Zero-order and Black-box Optimization

Jinglue Xu, Qi Sun, Peter Schwendeman, Stefan Nielsen, Edoardo Cetin, Yujin Tang

Combining diverse foundation models is promising, but weight-merging is limited by mismatched architectures and closed APIs. **Trinity** addresses this with a lightweight coordinator that orchestrates collaboration among large language models (LLMs). The coordinator, comprising a compact language model ($\approx 0.6$B parameters) and a lightweight head ($\approx 10$K parameters), is optimized with an evolutionary strategy for efficient and adaptive delegation. **Trinity** processes queries over multiple turns, where at each turn the coordinator assigns one of three roles (*Thinker*, *Worker*, or *Verifier*) to a selected LLM, effectively offloading complex skill acquisition from the coordinator itself. Extensive experiments demonstrate that **Trinity** consistently outperforms individual models and existing methods in various tasks, including coding, math, reasoning, and domain knowledge, while robustly generalizing to out-of-distribution tasks. On established benchmarks, **Trinity** achieves state-of-the-art performance, including a new record of $86.2\%$ on LiveCodeBench. Theoretical and empirical analyses highlight two key factors driving this success: (1) the coordinator’s hidden-state representations provide rich contextualization of inputs, and (2) under high dimensionality and strict budget constraints, the separable Covariance Matrix Adaptation Evolution Strategy algorithm provides substantial advantages over RL, imitation learning, and random search, leveraging potential block-$\varepsilon$-separability.

Stefan Nielsen, Edoardo Cetin, Peter Schwendeman, Qi Sun, Jinglue Xu, Yujin Tang

Powerful large language models (LLMs) from different providers have been expensively trained and finetuned to specialize across varying domains. In this work, we introduce a new kind of Conductor model trained with reinforcement learning to automatically discover powerful coordination strategies among LLMs. Our Conductor learns not only to design targeted communication topologies for effective agent-to-agent collaboration, but also to prompt engineer focused instructions to the LLMs to maximally leverage their individual capabilities. We show that, by learning optimal coordination strategies over pools of powerful worker LLMs, a 7B Conductor achieves significant performance gains beyond any individual worker, attaining state-of-the-art results in challenging reasoning benchmarks, such as LiveCodeBench and GPQA. By training with randomized agent pools, our conductor effectively adapts to arbitrary sets of open- and closed-source agents, meeting any user requirements. Furthermore, allowing the Conductor to select itself as a worker gives rise to recursive topologies, elevating performance with a new form of dynamic test-time scaling through online iterative adaptation. More broadly, ours is among the early work demonstrating language model coordination can be unlocked through RL, where powerful coordination strategies emerge naturally in LLMs through pure end-to-end reward maximization.

Applications · Time Series

Daojun Liang, Qi Li, Yinglong Wang, Jing Chen, Hu Zhang, Xiaoxiao Cui, Qizheng Wang, Shuo Li

Time series forecasting has long been dominated by advances in model architecture, with recent progress driven by deep learning and hybrid statistical techniques. However, as forecasting models approach diminishing returns in accuracy, a critical yet underexplored opportunity emerges: the strategic use of post-processing. In this paper, we address the last-mile gap in time-series forecasting, which is to improve accuracy and uncertainty without retraining or modifying a deployed backbone. We propose $\delta$-Adapter, a lightweight, architecture-agnostic way to boost deployed time series forecasters without retraining. $\delta$-Adapter learns tiny, bounded modules at two interfaces: input nudging (soft edits to covariates) and output residual correction. We provide local descent guarantees, $O(\delta)$ drift bounds, and compositional stability for combined adapters. Meanwhile, it can act as a feature selector by learning a sparse, horizon-aware mask over inputs to select important features, thereby improving interpretability. In addition, it can also be used as a distribution calibrator to measure uncertainty. Thus, we introduce a Quantile Calibrator and a Conformal Corrector that together deliver calibrated, personalized intervals with finite-sample coverage. Our experiments across diverse backbones and datasets show that $\delta$-Adapter improves accuracy and calibration with negligible compute and no interface changes.

Computer Vision · 3D Rendering & Reconstruction

Renjie Ding, Yaonan Wang, Min Liu, Jialin Zhu, Jiazheng Wang, Jiahao Zhao, Wenting Shen, Feixiang He, Xiang Chen

3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time novel view synthesis. As an explicit representation optimized through gradient propagation among primitives, optimization widely accepted in deep neural networks (DNNs) is actually adopted in 3DGS, such as synchronous weight updating and Adam with the adaptive gradient. However, considering the physical significance and specific design in 3DGS, there are two overlooked details in the optimization of 3DGS: (i) update step coupling, which induces optimizer state rescaling and costly attribute updates outside the viewpoints, and (ii) gradient coupling in the moment, which may lead to under- or over-effective regularization. Nevertheless, such a complex coupling is under-explored. After revisiting the optimization of 3DGS, we take a step to decouple it and recompose the process into: Sparse Adam, Re-State Regularization and Decoupled Attribute Regularization. Taking a large number of experiments under the 3DGS and 3DGS-MCMC frameworks, our work provides a deeper understanding of these components. Finally, based on the empirical analysis, we re-design the optimization and propose AdamW-GS by re-coupling the beneficial components, under which better optimization efficiency and representation effectiveness are achieved simultaneously.

Jeongjae Lee, Jong Chul YE

While reinforcement learning has advanced the alignment of text-to-image (T2I) models, state-of-the-art policy gradient methods are still hampered by training instability and high variance, hindering convergence speed and compromising image quality. Our analysis identifies a key cause of this instability: disproportionate credit assignment, in which the mathematical structure of the generative sampler produces volatile and non-proportional feedback across timesteps. To address this, we introduce Proportionate Credit Policy Optimization (PCPO), a framework that enforces proportional credit assignment through a stable objective reformulation and a principled reweighting of timesteps. This correction stabilizes the training process, leading to significantly accelerated convergence and superior image quality. The improvement in quality is a direct result of mitigating model collapse, a common failure mode in recursive training. PCPO substantially outperforms existing policy gradient baselines on all fronts, including the state-of-the-art DanceGRPO. Code is available at https://github.com/jaylee2000/pcpo/.

General Machine Learning · Everything Else

Michael Eichelbeck, Tim Voigt, Matthias Althoff

High-stakes, infrequent consumer decisions, such as housing selection, challenge conventional recommender systems due to sparse interaction, heterogeneous multi-criteria objectives, and high-dimensional features. This work presents an interactive preference elicitation framework utilizing preferential Bayesian optimization (PBO) to learn the unknown utility function of a user from pairwise comparisons that are integrated in real-time. To increase efficiency in a complex feature space, we learn the preference model in the latent space of an autoencoder (AE). Additionally, to mitigate a cold start, we obtain a personalized probabilistic prior through an automated user interview with a large language model (LLM). We evaluate the developed method on rental real estate datasets from two major European cities. The results show that executing PBO in the AE latent space improves final pairwise ranking accuracy by 12\%. For LLM-based preference prior generation, we find that direct, LLM-driven weight specification is outperformed by a static prior, while probabilistically weighted priors using LLMs achieve 25\% better pairwise accuracy.

Jing Yang, Qiyao Wei, Jiaxin Pei

Submissions are rising fast, and venues use different rules, data formats, and update times. As a result, signals of progress get split across places, and key moments (rebuttal, discussion, final decision) are easy to miss, making analysis hard. We present Paper Copilot, a system and scalable peer-review archive that pulls data from official sites, OpenReview, and opt-in forms into a single, standardized, versioned record with timestamps. This lets us track trends over time and compare venues, institutions, and countries in a consistent way. Using the archive for ICLR 2024/2025, we see larger score changes after rebuttal for higher-tier papers, reviewer agreement that dips during active discussion and tightens by the end, and in 2025 a sharper, mean-score–driven assignment of tiers with lower decision uncertainty than expected at that scale. We also state simple rules for ethics—clear sourcing and consent, privacy protection, and limits on use for closed venues. Together, we provide a clear, reusable base for tracking AI/ML progress, and, with this data, enable validation, benchmarking, and otherwise hard-to-run studies.

Deep Learning · Generative Models and Autoencoders

Chirag Modi, Jiequn Han, Eric Vanden-Eijnden, Joan Bruna

Transport-based methods have emerged as a leading paradigm for building generative models from large, clean datasets. However, in many scientific and engineering domains, clean data are often unavailable: instead, we only observe measurements corrupted through a noisy, ill-conditioned channel. A generative model for the original data thus requires solving an inverse problem at the level of distributions. In this work, we introduce a novel approach to this task based on Stochastic Interpolants: we iteratively update a transport map between corrupted and clean data samples using only access to the corrupted dataset as well as black box access to the corruption channel. Under appropriate conditions, this iterative procedure converges towards a self-consistent transport map that effectively inverts the corruption channel, thus enabling a generative model for the clean data. We refer to the resulting method as the self-consistent stochastic interpolant (SCSI). It (i) is computationally efficient compared to variational alternatives, (ii) highly flexible, handling arbitrary nonlinear forward models with only black-box access, and (iii) enjoys theoretical guarantees. We demonstrate superior performance on inverse problems in natural image processing and scientific reconstruction, and establish convergence guarantees of the scheme under appropriate assumptions.

Yingdong Shi, Zhiyu Yang, Changming Li, Jingyi Yu, Kan Ren

Vision models have demonstrated remarkable capabilities, yet their decision-making processes remain largely opaque. Mechanistic interpretability (MI) offers a promising avenue to decode these internal workings. However, existing interpretation methods suffer from two key limitations. First, they rely on the flawed activation-magnitude assumption, assuming that the importance of a neuron is directly reflected by the magnitude of its activation, which ignores more nuanced causal roles. Second, they are predominantly input-centric, failing to capture the causal mechanisms that drive a model's output. These shortcomings lead to inaccurate and unreliable internal representation interpretations, especially in cases of incorrect predictions. We propose MICLIP (Mechanism-Interpretability via Contrastive Learning), a novel framework that extends CLIP’s contrastive learning to align internal mechanisms of vision models with general semantic concepts, enabling interpretable and controllable representations. Our approach circumvents previous limitations by performing multimodal alignment between a model's internal representations and both its input concepts and output semantics via contrastive learning. We demonstrate that MICLIP is a general framework applicable to diverse representation unit types, including individual neurons and sparse autoencoder (SAE) features. By enabling precise, causal-aware interpretation, MICLIP not only reveals the semantic properties of a model's internals but also paves the way for effective and targeted manipulation of model behaviors.

General Machine Learning · Causality

Lue Tao, Tian-Zuo Wang, Yuan Jiang, Zhi-Hua Zhou

When a predictive model anticipates an undesired future event, a question arises: What can we do to avoid it? Resolving this forward-looking challenge requires determining the variables that positively influence the future, moving beyond the statistical *association* typically exploited for prediction. In this paper, we introduce a novel measure for evaluating the *influence* of actionable variables in successfully avoiding the undesired future. We quantify influence as the degree to which the success probability can be increased by altering variables under the principle of maximum expected utility. Our analysis demonstrates a counterintuitive insight: while related to *causality*, influential variables may not necessarily be those with strong intrinsic causal effects on the target event. In fact, it can be highly beneficial to alter a weak causal factor, or even a variable that is not an intrinsic factor at all. We provide a practical implementation for estimating the proposed measure and validate its utility through experiments on synthetic and real-world tasks.

Theory · Domain Adaptation and Transfer Learning

Xiao MA, Young D. Kwon, Pan Zhou, Dong Ma

Test-Time Adaptation (TTA) adapts a deployed model during online inference to mitigate the impact of domain shift. While achieving strong accuracy, most existing methods rely on backpropagation, which is memory and computation intensive, making them unsuitable for resource-constrained devices. Recent attempts to reduce this overhead often suffer from high latency or are tied to specific architectures such as ViT-only or CNN-only. In this work, we revisit domain shift from an embedding perspective. Our analysis reveals that domain shift induces three distinct structural changes in the embedding space: translation (mean shift), scaling (variance shift), and rotation (covariance shift). Based on this insight, we propose Progressive Embedding Alignment (PEA), a backpropagation-free and architecture-agnostic TTA approach. By applying a novel covariance alignment procedure at each intermediate layer, PEA efficiently corrects the embedding distortions with only two forward passes. Extensive experiments demonstrate that PEA achieves state-of-the-art performance in both accuracy and efficiency, while also proving versatile across different architectures including ViTs and CNNs.

Theory · Game Theory

Ioannis Anagnostides, Emanuel Tewolde, Brian Zhang, Ioannis Panageas, Vincent Conitzer, Tuomas Sandholm

Regret matching (RM)---and its modern variants---is a foundational online algorithm that has been at the heart of many AI breakthrough results in solving benchmark zero-sum games, such as poker. Yet, surprisingly little is known so far in theory about its convergence beyond two-player zero-sum games. For example, whether regret matching converges to Nash equilibria in potential games has been an open problem for two decades. Even beyond games, one could try to use RM variants for general constrained optimization problems. Recent empirical evidence suggests that they---particularly regret matching$^+$ (RM$^+$)---attain strong performance on benchmark constrained optimization problems, outperforming traditional gradient descent-type algorithms. We show that RM$^+$ converges to an $\epsilon$-KKT point after $O_\epsilon(1/\epsilon^4)$ iterations, establishing for the first time that it is a sound and fast first-order optimizer. Our argument relates the KKT gap to the accumulated \emph{regret}, two quantities that are entirely disparate in general but interact in an intriguing way in our setting, so much so that when regrets are bounded, our complexity bound improves all the way to $O_\epsilon(1/\epsilon^2)$. From a technical standpoint, while RM$^+$ does not have the usual one-step improvement property in general, we show that it does in a certain region that the algorithm will quickly reach and remain in thereafter. In contrast, our second main result establishes that RM, with or without alternation, can take an exponential number of iterations to reach a crude approximate solution even in two-player potential games. This represents the first worst-case separation between RM and RM$^+$. Our lower bound shows that convergence to coarse correlated equilibria in potential games is exponentially faster than convergence to Nash equilibria.

Theory · Reinforcement Learning and Planning

Yuyang Wu, Yifei Wang, Ziyu Ye, Tianqi Du, Stefanie Jegelka, Yisen Wang

Large Language Models (LLMs) increasingly rely on Chain-of-Thought (CoT) reasoning to solve complex problems. Contrary to the common belief that longer CoTs always improve performance, we demonstrate that **longer is not always better**. Across both real-world LLMs and theoretical models, task accuracy follows an inverted U-shaped curve with respect to CoT length: performance rises initially but declines once reasoning chains become too long. Through controlled experiments, we uncover **scaling behaviors of the optimal CoT length**: it increases with task difficulty but decreases with model capability. This exposes a significant mismatch with current practice, where supervised training often reuses the same CoT data across models and tasks without adaptivity. We further show that Reinforcement Learning (RL) can mitigate this gap by dynamically calibrating CoT length, thereby improving accuracy and offering a new perspective on differences between supervised fine-tuning and RL training. To explain these phenomena, we introduce an error-accumulation analysis that characterizes how reasoning errors propagate across steps and derives the scaling behaviors of CoT length observed empirically. Building on these insights, we show that training with optimally sized CoTs and applying length-aware filtering during inference yields substantial improvements in performance. Taken together, these findings establish a principled explanation of the ''overthinking'' effect and yield practical guidelines for calibrating CoT length in accordance with task complexity and model capability.

Applications · Language, Speech and Dialog

Minsu Kim, Se-Young Yun

While reinforcement learning from verifiable rewards (RLVR) typically has relied on a single binary verification signal, symbolic proof assistants in formal reasoning offer rich, fine-grained structured feedback. This gap between structured processes and unstructured rewards highlights the importance of feedback that is both dense and sound. In this work, we demonstrate that the Lean proof assistant itself can serve as a symbolic process oracle, supplying both outcome-level and fine-grained tactic-level verified feedback during training. Proof attempts are parsed into tactic sequences, and Lean's elaboration marks both locally sound steps and the earliest failing step, yielding dense, verifier-grounded credit signals rooted in type theory. We incorporate these structured rewards into a GRPO-style reinforcement learning objective with first-error propagation and first-token credit methods that balances outcome- and process-level advantages. Experiments with STP-Lean and DeepSeek-Prover-V1.5 show that tactic-level supervision outperforms outcome-only baselines in most settings, delivering improvements on benchmarks such as MiniF2F and ProofNet. Beyond empirical gains, our study highlights a broader perspective: symbolic proof assistants are not only verifiers at evaluation time, but can also act as process-level reward oracles during training. This opens a path toward reinforcement learning frameworks that combine the scalability of language models with the reliability of symbolic verification for formal reasoning.

Deep Learning · Graph Neural Networks

Adrián Arnaiz-Rodríguez, Federico Errica

After a renaissance phase in which researchers revisited the message-passing paradigm through the lens of deep learning, the graph machine learning community shifted its attention towards a deeper and practical understanding of message-passing's benefits and limitations. In this paper, we notice how the fast pace of progress around the topics of oversmoothing and oversquashing, the homophily-heterophily dichotomy, and long-range tasks, came with the consolidation of commonly accepted beliefs and assumptions -- under the form of universal statements -- that are not always true nor easy to distinguish from each other. We argue that this has led to ambiguities around the investigated problems, preventing researchers from focusing on and addressing precise research questions while causing a good amount of misunderstandings. Our contribution is to make such common beliefs explicit and encourage critical thinking around these topics, refuting universal statements via simple yet formally sufficient counterexamples. The end goal is to clarify conceptual differences, helping researchers address more clearly defined and targeted problems. The hope is to clarify the distinction between the different issues and promote separate but intertwined research directions to address them.

Optimization · Learning for Optimization

Zheng Zhang, Ziwei Shan, Kaitao Song, Yexin Li, Kan Ren

Process Reward Models (PRMs) have emerged as a promising approach to enhance the reasoning capabilities of large language models (LLMs) by guiding their step-by-step reasoning toward a final answer. However, existing PRMs either treat each reasoning step in isolation, failing to capture inter-step dependencies, or struggle to align process rewards with the final outcome. Consequently, the reward signal fails to respect temporal causality in sequential reasoning and faces ambiguous credit assignment. These limitations make downstream models vulnerable to reward hacking and lead to suboptimal performance. In this work, we propose Conditional Reward Modeling (CRM) that frames LLM reasoning as a temporal process leading to a correct answer. The reward of each reasoning step is not only conditioned on the preceding steps but also explicitly linked to the final outcome of the reasoning trajectory. By enforcing conditional probability rules, our design captures the causal relationships among reasoning steps, with the link to the outcome allowing precise attribution of each intermediate step, thereby resolving credit assignment ambiguity. Further, through this consistent probabilistic modeling, the rewards produced by CRM enable more reliable cross-sample comparison. Experiments across Best-of-N sampling, beam search and reinforcement learning demonstrate that CRM consistently outperforms existing reward models, offering a principled framework for enhancing LLM reasoning. In particular, CRM is more robust to reward hacking and delivers stable downstream improvements without relying on verifiable rewards derived from ground truth.

Social Aspects · Trustworthy Machine Learning

Robert Adrian Minut, Hazem Dewidar, Iacopo Masi

We reinterpret the final Large Language Model (LLM) softmax classifier as an Energy-Based Model (EBM), decomposing the sequence-to-sequence probability chain into multiple interacting EBMs at inference. This principled approach allows us to track “energy spills” during decoding, which we empirically show correlate with factual errors, biases, and failures. Similar to Orgad et al. (2025), our method localizes the exact answer token and subsequently tests for hallucinations. Crucially, however, we achieve this without requiring trained probe classifiers or activation ablations. Instead, we introduce two completely training-free metrics derived directly from output logits: **spilled energy**, which captures the discrepancy between energy values across consecutive generation steps that should theoretically match, and **marginalized energy**, which is measurable at a single step. Evaluated on nine benchmarks across state-of-the-art LLMs (including LLaMA, Mistral, and Gemma) and on synthetic algebraic operations (Qwen3), our approach demonstrates robust, competitive hallucination detection and cross-task generalization. Notably, these results hold for both pretrained and instruction-tuned variants without introducing any training overhead. Code available at [github.com/OmnAI-Lab/spilled-energy/](https://github.com/OmnAI-Lab/spilled-energy/)

Reinforcement Learning · Everything Else

Kejie He, Yi-Chen Li, Yang Yu

Model fusion has emerged as a promising research direction, offering a resource-efficient paradigm that leverages existing pre-trained models to circumvent the need for training from scratch. In this work, we investigate the fusion of models specifically adapted for decision-making tasks. This challenge divides into two distinct, yet related subproblems: the direct fusion of models that act as policy and the fusion of dynamics models that subsequently induce a policy. We suggest that these seemingly divergent subproblems can be unified through the lens of energy-based models (EBMs), which parameterizes a conditional distribution via an energy function where lower energy implies higher probability. Our framework, \textbf{EMFuse}, provides this convergence by leveraging the concept of energy as a common currency for fusion. For direct fusion of policies, such as those in language models, the output distribution is commonly softmax (Boltzmann), which essentially defines the negative logarithmic probability as an energy function. For dynamics models, existing works often train a set of models on the same dataset to obtain robust uncertainty estimation; such an ensemble approach leads to an exponential explosion in computational complexity when it comes to dynamics fusion across multiple sets of models. To overcome this, we introduce the Any-step Dynamics Energy-based Transition Model (ADETM), a novel architecture that performs efficient single-model-per-dataset uncertainty estimation with its energy-based backbone, thereby avoiding this computational explosion. Our EMFuse framework surpasses other baselines by 0.34\% to 6.63\% on single/cross domain discrete decision-making benchmarks, and achieved an extra 2.3 to 7.4 normalized points on average in D4RL MuJoCo continuous-control scenarios. Our code is available at https://github.com/LAMDA-RL/EMFuse.

Applications · Time Series

Yu Guoqi, Juncheng Wang, Chen Yang, Jing Qin, Angelica Aviles-Rivero, Shujun Wang

Accurate analysis of Medical time series (MedTS) data, such as Electroencephalography (EEG) and Electrocardiography (ECG), plays a pivotal role in healthcare applications, including the diagnosis of brain and heart diseases. MedTS data typically exhibits two critical patterns: **temporal dependencies** within individual channels and **channel dependencies** across multiple channels. While recent advances in deep learning have leveraged Transformer-based models to effectively capture temporal dependencies, they often struggle to model channel dependencies. This limitation stems from a structural mismatch: ***MedTS signals are inherently centralized, whereas the Transformer's attention is decentralized***, making it less effective at capturing global synchronization and unified waveform patterns. To bridge this gap, we propose **CoTAR** (Core Token Aggregation-Redistribution), a centralized MLP-based module tailored to replace the decentralized attention. Instead of allowing all tokens to interact directly, as in attention, CoTAR introduces a global core token that acts as a proxy to facilitate the inter-token interaction, thereby enforcing a centralized aggregation and redistribution strategy. This design not only better aligns with the centralized nature of MedTS signals but also reduces computational complexity from quadratic to linear. Experiments on five benchmarks validate the superiority of our method in both effectiveness and efficiency, achieving up to a **12.13%** improvement on the APAVA dataset, with merely 33% memory usage and 20% inference time compared to the previous state-of-the-art. Code and all training scripts are available in this [**Link**](https://github.com/Levi-Ackman/TeCh).

Applications · Time Series

SheoYon Jhin, Noseong Park

Anomaly detection in time series is essential for applications from industrial monitoring to financial risk management. Recent methods --- including forecasting error models, representation learning, augmentation, and weak-label learning --- have achieved strong results for specific anomaly types such as sudden point or gradual collective anomalies. While many prior works report window-level metrics that may mask errors, several recent methods evaluate at the point level as well. Our goal is to use a stricter point-wise protocol to make masking effects explicit. We introduce FOLD (Point-wise Anomaly Detection via fold-bifurcation), a framework that reframes detection as tracking a system’s proximity to a critical transition. FOLD extracts stress signals from a forecasting model and integrates them with a fold-bifurcation inspired ODE to produce the risk state, flagging anomalies once it crosses a threshold calibrated on normal data. This requires no anomaly labels and no additional detector training, enabling a parameter-free and efficient detection process. By modeling anomalies as stress accumulation toward a tipping point, FOLD naturally aligns with point-wise detection, providing a unifying and interpretable perspective that complements type-specific methods. Experiments on 40 benchmarks against 34 state-of-the-art baselines show that FOLD achieves competitive or superior performance, with particular strength under strict point-wise evaluation.