论文检索

Applications · Time Series

Zehao Liu, Pengfei Jiao, Yuhan Wu, Jianqi Yang, Yuyu Yin

Although causal discovery from multivariate time series is widely used, it remains challenging under noise. Convergent cross mapping (CCM) infers causality by reconstructing shadow manifolds via time-delay embedding (TDE) and evaluating cross-map skill between manifolds. Despite Takens’ theorem guarantees in ideal settings, TDE effectively attempts to recover system state from a single noisy view, often yielding geometrically degraded manifolds and unreliable distance-based neighborhoods, which in turn weakens causal identification. We propose TopoDistill, a topology-informed knowledge distillation framework that improves univariate shadow-manifold reconstruction by aligning local neighborhood structure to a multivariate system representation. A global embedder trained on multivariate observations captures a global attractor representation, while a delay embedder is distilled to produce embeddings whose neighborhood distributions match the global topology. This cross-view alignment yields smoother and more reliable neighborhoods, improving cross mapping under noise while maintaining specificity against spurious correlations. Theoretical analysis and experimental results demonstrate that our method enables effective causal discovery.

Deep Learning · Theory

Ari Pakman, Lior Kreimer, Yakir Berchenko

Modern deep neural networks often contain far more parameters than needed to fit their training data, yet they achieve impressive generalization. A common explanation of this success is the implicit bias of stochastic gradient descent (SGD). An alternative volume hypothesis posits that, within low training-loss regions, loss-landscape basins leading to strong generalization occupy much larger regions of weight space than basins that generalize poorly, and therefore SGD is simply more likely to land in the former. Recent experimental explorations of this idea present seemingly contradictory results. While in one set of experiments randomly sampling the network weights until achieving zero training error yielded poor generalization, molecular-dynamics density estimates supported the volume hypothesis. We observe that these experiments were performed at different dataset size regimes, and explore an intermediate regime using the Replica Exchange Wang–Landau algorithm to estimate the joint density of states over training and test accuracies in binary networks. Across several architectures and datasets, we show that the generalization advantage of SGD over random sampling training diminishes as the training data size grows, suggesting a resolution of the paradox.

Deep Learning · Large Language Models

Linzheng Chai, Jian Yang, Jiajun Wu, Ensheng Shi, Xianglong Liu

Current reinforcement learning (RL) methods for code generation are predominantly optimized on Python, showing weak generalization to other programming languages (PLs). Although leveraging multilingual solutions offers richer semantics and a wider search landscape, naive independent training across languages suffers from optimization imbalance and fails to effectively transfer knowledge from high-resource languages. We propose Group Cross-lingual Relative Policy Optimization (GXPO), which forms training groups by generating solutions for the same problem in multiple PLs and jointly optimizes language-specific and cross-language signals, enabling more balanced optimization and improved transfer to low-resource PLs. We additionally introduce Multilingual LiveCodeBench (ML-LCB), extending LiveCodeBench to a unified multilingual evaluation setting. On ML-LCB across 8 PLs, GXPO consistently improves performance, with pronounced gains on low-resource PLs, demonstrating scalable multilingual RL for language-consistent code generation.

Deep Learning · Theory

Yichao Cai, Zhen Zhang, Yuhang Liu, Javen Qinfeng Shi

While InfoNCE powers modern contrastive learning, its geometric mechanisms remain under-characterized beyond the canonical alignment--uniformity decomposition. We present a measure-theoretic framework that models learning as the evolution of representation measures on a fixed embedding manifold. By establishing value and gradient consistency in the large-batch limit, we bridge the stochastic objective to explicit deterministic energy landscapes, uncovering a fundamental geometric bifurcation between the unimodal and multimodal regimes. In the unimodal setting, the intrinsic landscape is strictly convex with a unique Gibbs equilibrium; here, entropy acts merely as a tie-breaker, clarifying "uniformity" as a constrained expansion within the alignment basin. In contrast, the symmetric multimodal objective contains a persistent negative symmetric divergence term that remains even after kernel sharpening. We show that this term induces barrier-driven co-adaptation, enforcing a population-level modality gap as a structural geometric necessity rather than an initialization artifact. Our results shift the analytical lens from pointwise discrimination to population geometry, offering a principled basis for diagnosing and controlling distributional misalignment.

Deep Learning · Theory

Zheng-An Chen, Pengxiao Lin, Zhi-Qin John Xu, Tao Luo

Transformer-based models have achieved remarkable success across a wide range of domains, yet our understanding of their training dynamics remains limited. In this work, we identify a recurrent focus–dilution cycle in attention learning and provide a rigorous explanation in a one-layer Transformer setting for Markovian data via gradient-flow analysis. Using stage-wise linearization around critical points, we show that a single focus–dilution cycle can be decomposed into a sequence of distinct stages. First, embedding and projection rapidly condense to a rank-one structure, while attention parameters remain effectively frozen. Then, the attention parameters begin to increase, inducing a frequency-driven focus toward high-frequency tokens. As attention continues to evolve, it generates next-order perturbations in embeddings, leading to a mass-redistribution mechanism that progressively dilutes this focus. Finally, small asymmetries among low-frequency tokens lift a degenerate critical point, opening new embedding directions and initiating the next cycle. Experiments on synthetic Markovian data as well as WikiText and TinyStories corroborate the predicted stages and cyclical dynamics.

Deep Learning · Theory

Devansh Arpit

We identify test prediction variance (TPV)—the first-order sensitivity of model outputs to parameter perturbations around a trained solution—as a unifying quantity that links several classical observations about generalization in deep networks. TPV is a fully label-free object whose trace form $\mathrm{Tr}(H_{\mathrm{eff}} C)$ separates the geometry of the trained model $H_{\mathrm{eff}}$ from the specific perturbation mechanism $C$, allowing a broad family of parameter perturbations like SGD noise, label noise, finite-precision noise, and other post-training perturbations to be analyzed under a single framework. Theoretically, we show that TPV estimated on the training set converges to its test-set value in the overparameterized limit, providing the first result that prediction variance under local parameter perturbations can be inferred from training inputs alone, and this stability is decoupled from generalization performance. Empirically, TPV exhibits a striking stability across datasets and architectures even for extremely narrow networks. Further, TPV correlates well with test loss, serving as a training-set based predictive metric for generalization.

Theory · Learning Theory

Zheng Zhang, Jiaye Yang, Qingjie Guo, Jiangrong Shen, Long Chen, Qi Xu

Gradient-based optimization in class-incremental learning (CIL) often faces the plasticity–stability dilemma, since continuous weight updates can distort decision boundaries learned from earlier tasks. We revisit this problem from the viewpoint of stochastic geometric memory allocation and propose BTSP-CAM, a gradient-free memory system that instantiates theoretical insights from the hippocampal simpleBTSP model into a practical algorithm. Rather than fine-tuning a frozen encoder by backpropagation, BTSP-CAM externalizes plasticity into a binary synaptic matrix that evolves through local stochastic bit-flip updates. A trace-gated plateau process, driven by eligibility traces together with familiarity and collision signals, modulates when and where synapses are rewritten and suppresses cross-class interference in Hamming space. The resulting geometric memory states are mapped to semantic logits through a CA1-like competitive layer and a closed-form ridge readout, enabling fast consolidation after each task. Empirically, BTSP-CAM rivals gradient-based methods in a strictly exemplar-free setting and consistently boosts SOTA baselines as a lightweight plugin. Mechanistic analysis validates our geometric theory, confirming that stochastic repulsion actively bounds class overlap and stabilizes decision margins.

Helena Casademunt, Caden Juang, Adam Karvonen, Samuel Marks, Senthooran Rajamanoharan, Neel Nanda

Fine-tuning large language models (LLMs) can lead to unintended out-of-distribution generalization. Standard approaches to this problem rely on modifying the training data, for example by adding data that better specify the intended generalization. However, this is not always practical. We introduce Concept Ablation Fine-Tuning (CAFT), a technique that leverages interpretability tools to control how LLMs generalize from fine-tuning, without needing to modify the training data or otherwise use data from the target distribution. Given a set of directions in an LLM's latent space corresponding to undesired concepts, CAFT works by ablating these concepts with linear projections during fine-tuning, steering the model away from unintended generalizations. We successfully apply CAFT to three fine-tuning tasks, including emergent misalignment, a phenomenon where LLMs fine-tuned on a narrow task generalize to give egregiously misaligned responses to general questions. Without any changes to the fine-tuning data, CAFT reduces misaligned responses by 10x without degrading performance on the training distribution. Overall, CAFT represents a novel approach for steering LLM generalization without modifying training data.

Zihan (Zenus) Wang, Chi Gui, Xing Jin, Qineng Wang, Licheng Liu, Kangrui Wang, Shiqi Chen, Linjie Li, Zhengyuan Yang, Pingyue Zhang 等

In closed-loop multi-turn agent reinforcement learning, LLM agents exhibit reasoning collapse, where reasoning shift toward generic templates, weakly coupled to the inputs. We firstly identify that such collapse is easy to miss with entropy or surface diversity metrics since reasoning text still varies but becomes input-agnostic. We then propose an information-theoretic decomposition of reasoning variable $Z$'s variation into conditional entropy $H(Z \mid X)$ (randomness under same input) and mutual information (MI) $I(X; Z)$ (input dependence). Template collapse occurs when $H(Z \mid X)$ stays high while $I(X; Z)$ drops, yielding diverse-looking but generic reasoning. To make $I(X; Z)$ a reproducible and sanity-checkable diagnostic, we further introduce an MI-style retrieval protocol treating each reasoning trace $Z$ as a query to retrieve its source $X$ from a minibatch; accuracy degrades toward chance under collapse. We thus provide a signal-to-noise ratio explanation for why $I(X; Z)$ drops: when within-input reward variance $\mathrm{Var}(R \mid X)$ is low, task gradients weaken and input-agnostic regularizers (KL, entropy) dominate, flattening cross-input differences. Finally, we propose reward-variance-aware filtering to prioritize high-signal updates. Across multi-turn environments, model scales, and modalities (including VLMs), this improves input dependence, stability, and performance while remaining competitive with state-of-the-art stabilization baselines.

Deep Learning · Theory

Tianyu Pang, Vignesh Kothapalli, Shenyang Deng, Haohui Wang, Dawei Zhou, Yaoqing Yang

We study optimal learning-rate selection in two-layer and three-layer linear neural networks trained to learn a single-index target function. In particular, we derive the exact closed-form expressions for the gradients and test loss after one and two steps of gradient descent, enabling a precise characterization of early training dynamics. We characterize how learning rates should scale under the gradient approximation in the first two steps, and prove that performing updates with this approximation yields a tractable surrogate loss with a tight, small approximation error. This formulation enables the theoretical analysis of layer-wise learning rates and reveals a distinct early-training regime: test loss can be minimized by unequal learning rates at the initial step, while equal learning rates become optimal in subsequent steps. Our numerical experiments validate these theoretical predictions and demonstrate the importance of balancing layer-wise learning-rate during early training.

Deep Learning · Foundation Models

Zihan (Zenus) Wang, Chi Gui, Xing Jin, Qineng Wang, Licheng Liu, Kangrui Wang, Shiqi Chen, Linjie Li, Zhengyuan Yang, Pingyue Zhang 等

In closed-loop multi-turn agent reinforcement learning, LLM agents exhibit reasoning collapse, where reasoning shift toward generic templates, weakly coupled to the inputs. We firstly identify that such collapse is easy to miss with entropy or surface diversity metrics since reasoning text still varies but becomes input-agnostic. We then propose an information-theoretic decomposition of reasoning variable $Z$'s variation into conditional entropy $H(Z \mid X)$ (randomness under same input) and mutual information (MI) $I(X; Z)$ (input dependence). Template collapse occurs when $H(Z \mid X)$ stays high while $I(X; Z)$ drops, yielding diverse-looking but generic reasoning. To make $I(X; Z)$ a reproducible and sanity-checkable diagnostic, we further introduce an MI-style retrieval protocol treating each reasoning trace $Z$ as a query to retrieve its source $X$ from a minibatch; accuracy degrades toward chance under collapse. We thus provide a signal-to-noise ratio explanation for why $I(X; Z)$ drops: when within-input reward variance $\mathrm{Var}(R \mid X)$ is low, task gradients weaken and input-agnostic regularizers (KL, entropy) dominate, flattening cross-input differences. Finally, we propose reward-variance-aware filtering to prioritize high-signal updates. Across multi-turn environments, model scales, and modalities (including VLMs), this improves input dependence, stability, and performance while remaining competitive with state-of-the-art stabilization baselines.

Applications · Computer Vision

Dongxing Mao, Alex Jinpeng Wang, weiming Han, Jiawei Zhang, Zhuobai Dong, Linjie Li, Lin Yiqi, Zhengyuan Yang, Libo Qin, Fuwei Zhang 等

Text-conditioned image generation has made rapid progress, yet rendering images with long-form text remains challenging due to the limitations of existing datasets, which predominantly focus on short and simple text. We introduce TextAtlas5M, a large-scale dataset designed to evaluate long-text rendering, where “long text” encompasses not only textual length but also layout complexity and semantic richness. TextAtlas5M contains 5 million generated and collected images across diverse data types, enabling comprehensive evaluation of large-scale generative models. We further curate 4,000 human-improved test cases (TextAtlasEval) spanning four domains, forming one of the most extensive benchmarks for text rendering. Evaluations show that TextAtlas5M poses substantial challenges even for state-of-the-art proprietary models (e.g., GPT-4o), with significantly larger gaps observed for open-source models. Training on TextAtlas5M consistently improves text rendering for both diffusion-based and autoregressive models, demonstrating its effectiveness for advancing text-rich image generation.

Deep Learning · Other Representation Learning

Core Francisco Park

While neural representations are central to modern deep learning, the conditions governing their geometry and their roles in downstream adaptability remain poorly understood. We develop a framework clearly separating the underlying world, the data generation process and the resulting model representations to study these questions in a controlled setup: 5,075 city coordinates define the world and 7 geometric tasks generate the training data for autoregressive Transformer training. We find that different tasks give rise to qualitatively and quantitatively distinct world representation geometries. However, multi-task training drives convergence of world representations: models trained on non-overlapping tasks develop aligned geometric representations, providing controlled evidence for the Multitask Scaling Hypothesis of the Platonic Representation Hypothesis. To study adaptation, we pretrain models on all tasks and all cities, then test whether new entities can be consistently integrated into the representation space via fine-tuning. Surprisingly, we find that despite multi-task pretraining, some tasks, which we call divergent, actively harm the representational integration of new entities. Our results show that training on multiple relational tasks reliably produces convergent world representations, but some lurking divergent tasks can catastrophically harm new entity integration via fine-tuning.

Deep Learning · Large Language Models

Nuoya Xiong, Yuhang Zhou, Hanqing Zeng, Zhaorun Chen, Furong Huang, Shuchao Bi, Lizhu Zhang, Zhuokai Zhao

Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a single general-purpose model typically requires scaling to sizes that are prohibitively expensive to train and deploy. On the other hand, while smaller domain-specialized models are much more efficient, they struggle to generalize beyond their training distributions. To address this dilemma, we propose FusionRoute, a robust and effective token-level multi-LLM collaboration framework in which a lightweight router simultaneously (i) selects the most suitable expert at each decoding step and (ii) contributes a complementary logit that refines or corrects the selected expert’s next-token distribution via logit addition. Unlike existing token-level collaboration methods that rely solely on fixed expert outputs, we provide a theoretical analysis showing that pure expert-only routing is fundamentally limited: unless strong global coverage assumptions hold, it cannot in general realize the optimal decoding policy. By augmenting expert selection with a trainable complementary generator, FusionRoute expands the effective policy class and enables recovery of optimal value functions under mild conditions. Empirically, across both Llama-3 and Gemma-2 families and diverse benchmarks spanning mathematical reasoning, code generation, and instruction following, FusionRoute outperforms both sequence- and token-level collaboration, model merging, and direct fine-tuning, while remaining competitive with domain experts on their respective tasks.

Deep Learning · Large Language Models

Ziqiu Luo, Jianmin Liu, Yukai Miao, Li Chen, Dan Li

While self-consistency methods have emerged as a promising approach to enhance the correctness of large language model (LLM) outputs by aggregating multiple stochastic samples, they suffer from two critical limitations, resulting in high computation cost. First, they evaluate output consistency monolithically, failing to efficiently combine partially correct answers across multiple samples. Second, they use static stopping criteria that cannot adapt to varying task complexities and model capabilities, resulting in suboptimal computational efficiency. In this work, we present Task-and-Model-Aware Fractal-Consistency (TMAFC), a novel self-consistency framework that addresses these limitations through two key innovations: (1) Fractal-Consistency, which evaluates the output consistency at the granularity of output components to effectively combine partial correct answers across samples, and (2) Adaptive Stopping Criteria Calibration (ASCC), which dynamically adjusts sampling stopping criteria based on real-time assessment of both task difficulty and LLM capability. Through extensive experiments on diverse question-answering benchmarks, we demonstrate that TMAFC achieves superior efficiency-accuracy trade-offs, reducing sample cost by up to 55\% while maintaining competitive accuracy compared to state-of-the-art baselines.

Deep Learning · Large Language Models

Xinghao Chen, Chak Tou Leong, Wenjin Guo, Jian Wang, Wenjie Li, Xiaoyu Shen

Latent Chain-of-Thought (CoT) aims to internalize reasoning into continuous hidden states, promising to transcend the computational bottlenecks of explicit tokens. However, the precise mechanisms ensuring its validity remain opaque. To bridge this gap, we establish an Information-Theoretic Framework that dissects supervision into Trajectory Control and State Alignment. Our analysis identifies structural scaffolding as the fundamental prerequisite for valid latent dynamics, and demonstrate that Outcome Supervision falters due to optimization barriers, while Process Supervision succeeds by minimizing conditional entropy, thereby enforcing trajectory predictability. And we expose a divergence in alignment strategies: rigid Geometric Compression acts as a destructive prior that collapses the reasoning manifold, whereas Generative Reconstruction serves as a flexible semantic tether, optimizing for reconstructibility to preserve the intrinsic dimensionality of the latent space. To quantify these dynamics, we introduce the Unified Latent-MI Probe (ULP), which unveils a strict Information-Performance Binding: reasoning accuracy is deeply correlated with the mutual information retained in the latent chain.Ultimately, we advocate for a paradigm shift from geometric imitation to mutual information maximization to counter the information decay inherent in autoregressive generation.

Deep Learning · Large Language Models

Yuejun Jiao, Jun Xia, Yanxin Yang, Yonghao Yang, Hao Shen, Mingsong Chen

Automatic Prompt Optimization (APO) enables Large Language Models (LLMs) to adapt to specific tasks while minimizing manual engineering costs. However, since existing APO approaches either rely solely on multi-round iterative procedures or use model-specific generators tailored to optimizing prompts for a single model and objective, they are not readily applicable to auto-routing scenarios, which require operating over diverse LLMs and juggling multiple, often competing, trade-offs. To address this issue, we propose TAMPO, a novel task- and model-aware APO framework for auto-routing in LLM-based systems. Specifically, to reflect performance variation across a broad range of tasks and models, we construct a comprehensive heterogeneity-aware dataset for training an uncertainty-aware reward model. Serving as an offline proxy, this reward model can greatly mitigate reward hacking, allowing TAMPO to learn an optimal multi-objective conditional policy for robust prompt generation. Based on the user requirements encoded in our defined preference vector, this policy enables flexible control over prompt generation, supporting a cost-effective deployment strategy. Extensive experiments across 86 tasks demonstrate that TAMPO effectively maintains performance stability across diverse tasks and models, providing a robust, controllable solution for auto-routing in various LLM-based systems.

Deep Learning · Large Language Models

Xuanze Zhao, Hongcheng Ding, Jing Jin, LIU XUANHUANG, Shamsul Abdullah, Deshinta Dewi

Full fine-tuning of large language models (LLMs) incurs prohibitive computational and storage costs. Parameter-efficient fine-tuning (PEFT) addresses this limitation, with Low-Rank Adaptation (LoRA) gaining widespread adoption due to its simplicity and zero inference overhead. However, LoRA and its variants typically rely on uniform rank allocation or a single importance metric such as gradient magnitude or output sensitivity to guide rank distribution. This approach fails to recognize that gradient magnitude and output contribution are decoupled properties, leading to suboptimal allocation where critical layers are under-provisioned while less important ones waste capacity. To address this challenge, we propose COBRA, a principled framework integrating dual importance factors for adaptive rank allocation. COBRA operates in three stages: (1) layer conductance attribution quantifies each layer's contribution via path-integral attribution; (2) dual-factor aggregation combines contribution with adaptation demand, producing the TA-LC distribution; and (3) Bayesian rank allocation translates this distribution into optimal heterogeneous ranks via variational optimization. Layer conductance provides layer-level interpretability by explicitly quantifying how much each layer contributes to predictions without redundancy, directly aligning with the granularity of rank allocation decisions and enabling principled cross-layer comparison for rank distribution. Experiments across diverse architectures and tasks demonstrate that COBRA consistently outperforms existing methods, achieving up to 1.6 points improvement on GLUE and 6.6\% average gain in high-rank regimes under comparable parameter budgets.

General Machine Learning · Causality

Panayiotis Panayiotou, Audrey Poinsot, Alessandro Leite, Nicolas CHESNEAU, Marc Schoenauer, Özgür Şimşek

Causal machine learning (Causal ML) aims to answer "what if" questions using machine learning algorithms, making it a promising tool for high-stakes decision-making. Yet, empirical evaluation practices in Causal ML remain limited. Existing benchmarks often rely on a handful of hand-crafted or semi-synthetic datasets, leading to brittle, non-generalizable conclusions. To bridge this gap, we introduce CausalProfiler, a synthetic benchmark generator for Causal ML methods. Based on a set of explicit design choices about the class of causal models, queries, and data considered, the CausalProfiler randomly samples causal models, data, queries, and ground truths constituting the synthetic causal benchmarks. In this way, Causal ML methods can be rigorously and transparently evaluated under a variety of conditions. This work offers the first random generator of synthetic causal benchmarks with coverage guarantees and transparent assumptions operating on the three levels of causal reasoning: observation, intervention, and counterfactual. We demonstrate its utility by evaluating several state-of-the-art methods under diverse conditions and assumptions, both in and out of the identification regime, illustrating the types of analyses and insights the CausalProfiler enables.

Social Aspects · Accountability, Transparency, and Interpretability

Shuhang Lin, Chuhao Zhou, Xiao Lin, Zihan Dong, Kuan Lu, Zhencan Peng, Jie Yin, Dimitris Metaxas

While Conformal Prediction (CP) offers a principled framework for producing prediction sets with statistical guarantees, prior methods suffer from critical limitations in both calibration validity and score discriminability, resulting in violated coverage guarantees and excessively large prediction sets. To address these pitfalls, we propose Conformal Path Reasoning (CPR), a trustworthy KGQA framework with two key innovations. First, we perform query-level conformal calibration over path-level scores, preserving the exchangeability while generating path prediction sets. Second, we introduce the Residual Conformal Value Network (RCVNet), a lightweight module trained via PUCT-guided exploration to learn discriminative path-level nonconformity scores. Experiments on benchmarks show that CPR significantly improves the Empirical Coverage Rate by 34% while reducing average prediction set size by 40% compared to conformal baselines. These results validate the efficacy of CPR in satisfying coverage guarantees with substantially more compact answer sets.