Legal judgment prediction (LJP) is important for supporting judicial decision-making and improving judicial efficiency. Existing LLM-based approaches often rely on manually specified reasoning pipelines or external legal structures, while feedback-based methods typically provide generic signals of correctness or coherence rather than case-specific legal supervision. We propose IALR: Instance-Adaptive Legal Rubric Supervision for Legal Judgment Prediction. For each training instance, IALR contrasts model responses of varying quality and distills their legally consequential failures into a weighted Instance-Adaptive Legal Rubric. These rubrics convert judgment-relevant conditions into discriminative preference signals for Direct Preference Optimization, enabling the model to learn charge prediction without following a predefined reasoning paradigm. At inference time, IALR requires no additional evaluator or correction module. Experiments on four Chinese criminal charge-prediction benchmarks show that IALR consistently improves Qwen2.5-7B, yielding average absolute accuracy gains of 5.68% in-domain and 8.73% out-of-domain. These results demonstrate the effectiveness and cross-domain transferability of Instance-Adaptive Legal Rubric supervision. The code and data are available at https://github.com/Baii668/IALR.git.

Existing causal datasets primarily focus on the commonsense domain, where the questions mainly involve simple, single-hop direct causal relationships. When models possess the corresponding knowledge, even if they cannot understand the causal relationships, they can directly arrive at the correct answers through knowledge matching. However, LLMs often perform poorly when answering questions with complex causal structures and domain-specific expertise. To address the above challenges, we propose MDC-Bench, a multidisciplinary causal evaluation benchmark. MDC-Bench adopts a three-level causal framework consisting of 4 core causal tasks, while its sample content covers 7 representative disciplines and diverse causal structures. In view of the limited coverage of multidisciplinary knowledge during the pre-training phase, the model cannot answer questions relying on knowledge matching. The diverse causal structures force the models to grasp the internal causal logic. We also increase the task complexity through methods such as compound causal operations, aiming to enhance the discriminability among models. MDC-Bench achieves the improvement in terms of domain specialization, structural diversity, and task complexity. Through extensive evaluation, we observe that even the advanced models have substantial room for improvement. MDC-Bench not only establishes a standardized baseline for causal research but also provides valuable insights for the applying LLMs in multiple domains.

While reasoning-augmented large language models (RLLMs) significantly enhance complex task performance through extended reasoning chains, they inevitably introduce substantial unnecessary token consumption—particularly for simpler problems where Short Chain-of-Thought (Short CoT) suffices. This ”overthinking” phenomenon leads to inefficient resource usage without proportional accuracy gains. To address this issue, we propose Self-Route, a dynamic reasoning framework that automatically selects between general and reasoning modes based on model capability estimation. Our approach introduces a lightweight pre-inference stage to extract capability-aware embeddings from hidden layer representations, enabling real-time evaluation of the model’s ability to solve problems. We further construct Gradient-10K, a model difficulty estimation-based dataset with dense complexity sampling, to train the router for precise capability boundary detection. Extensive experiments demonstrate that Self-Route achieves comparable accuracy to reasoning models while reducing token consumption by 30-55% across diverse benchmarks. The proposed framework demonstrates consistent effectiveness across models with different parameter scales and reasoning paradigms, highlighting its general applicability and practical value.

Interactive large language model agents are often specialized to a single environment and transfer poorly across others. We study training-free consolidation of such specialists into a single generalist checkpoint. A key difficulty in this setting is that, in multi-turn agents, small merge errors on role-critical outputs can trigger long-horizon failure cascades. We therefore propose Agent-Role Merging (ARM), a neuron-level training-free framework that selects a merged backbone with an answer-free activation-overlap signal and then repairs residual gaps through conflict-aware transplantation of a small set of role-salient neurons. Our analyses further show that role-conditioned tracing yields more benchmark-specific neuron sets, enabling more localized repair. Across Qwen3-8B and Qwen2.5-7B expert pools, ARM produces the strongest balanced merged generalist among compared training-free baselines, improving both average performance and worst-suite robustness while preserving out-of-domain generalization. These results suggest that targeted neuron-level edits offer a practical route to robust multi-turn agent merging without end-to-end retraining.

Multi-agent debate systems are used to competitively discuss and resolve complex tasks. However, existing approaches often exhibit pseudo-debate behavior: agents either ignore opponents’ arguments or compromise too readily without critical evaluation. To address this, we propose a three-stage debate framework grounded in abstract argumentation theory. First, Debate Structuring uses principled prompts to elicit well-formed debate interactions. Second, Debate Imitation filters high-quality debates to fine-tune LLMs on proper argumentative conduct. Third, Debate Internalization employs a debate-aware reinforcement learning objective to instill adversarial reasoning patterns. To better quantify debate capability, we introduce two novel metrics: Rebuttal Engagement Score (RES), assessing how thoroughly models engage with opposing arguments, and Belief Governance Score (BGS), evaluating whether models appropriately retain or revise beliefs based on argument strength. Extensive evaluations across six benchmarks show that our framework not only improves task performance but also cultivates more authentic and robust debate behavior in LLMs. Our work contributes a theory-inspired training framework and evaluation suite for advancing multi-agent debate systems.

Translating brain signals into text could restore communication for people with severe paralysis, yet practically usable systems to date rely on invasive electrocorticography (ECoG). Electroencephalography (EEG) offers a non-invasive alternative, and EEG-to-text (EEG2Text) has been widely explored. Interestingly, however, EEG2Text models generally rely on teacher-forcing evaluation; without it, they fail to generate meaningful decoding. This reliance prevents EEG2Text from being applied in real-world, non-academic settings. This has fueled numerous debates about whether EEG2Text is a meaningful direction, by extension, and whether EEG truly contains decodable linguistic information. Here, using a neuropsychology-informed paradigm, we find that existing EEG2Text benchmarks have neglected EEG instability, a flaw that has confounded inference and sparked debate. Our experiments furnish key evidence for the feasibility of teacher-forcing-free EEG2Text decoding. Accordingly, we assemble the Corpus OF Eeg-To-Text (COFETT) using a 128-channel high-density EEG cap, providing a benchmark dedicated to evaluating EEG2Text models. In comparisons with multiple existing benchmarks, COFETT achieves SOTA ability to distinguish among model performances and enables robust, teacher-forcing-free evaluation, thereby opening a path toward practical EEG2Text applications. COFETT is open sourced in https://github.com/baoyudu/COFETT.

While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effective mechanisms for data allocation between these stages remain largely underexplored. Current data arbitration strategies often rely on surface-level heuristics that fail to diagnose intrinsic learning needs. Since SFT targets pattern consolidation through imitation while RL drives structural adaptation via exploration, misaligning data with these functional roles causes severe optimization interference. We propose PRISM, a dynamics-aware framework grounded in Schema Theory that arbitrates data based on its degree of cognitive conflict with the model’s existing knowledge. By analyzing the spatial geometric structure of gradients, PRISM identifies data triggering high spatial concentration as high-conflict signals that require RL for structural restructuring. In contrast, data yielding diffuse updates is routed to SFT for efficient consolidation. Extensive experiments on WebShop and ALFWorld demonstrate that PRISM achieves a Pareto improvement, outperforming state-of-the-art hybrid methods while reducing computational costs by up to 3.22 ×. Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.

Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is primarily confined to domains with verifiable ground truths. Extending GRPO to open-domain settings remains a critical challenge, as unconstrained generation entails multi-faceted and often conflicting objectives—such as creativity versus factuality—where rigid, static reward scalarization is inherently suboptimal. To address this, we propose MAESTRO (Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization), which introduces a meta-cognitive orchestration layer that treats reward scalarization as a dynamic latent policy, leveraging the model’s terminal hidden states as a semantic bottleneck to perceive task-specific priorities. We formulate this as a contextual bandit problem within a bi-level optimization framework, where a lightweight Conductor network co-evolves with the policy by utilizing group-relative advantages as a meta-reward signal. Across seven benchmarks, MAESTRO consistently outperforms single-reward and static multi-objective baselines, while preserving the efficiency advantages of GRPO, and in some settings even reducing redundant generation.

Entailment trees are essential for enhancing interpretability and transparency in tasks like question answering and natural language understanding. However, existing approaches often lack logical consistency, as they rely on static reward structures or ignore the intricate dependencies within multi-step reasoning. To address these limitations, we propose a method that integrates natural logic principles into reinforcement learning, enabling dynamic reward computation to guide entailment tree generation. Our approach ensures logical consistency across reasoning steps while improving interpretability and generalization. Experiments on EntailmentBank demonstrate significant improvements over state-of-the-art methods, highlighting the effectiveness of natural logic in structured reasoning.

LLMs’ performance on complex tasks is still unsatisfactory. A key issue is that presently LLMs learn in a data-driven schema, while the instructions about these complex tasks are both scarce and hard to collect or construct. On the contrary, a prominent phenomenon is that LLMs can learn rather fast on simpler tasks with adequate prior knowledge captured during pretraining stage. Thus, if the prerequisite and mechanism of such rapid generalization could be elucidated, it could enhance the efficiency and effectiveness of the LLM’s ability to learn complex tasks. Thus, in this paper, we employ a gradient-based method, to dissect the process that the SFT process adapts LLMs to downstream tasks via the perspective of attention patterns. We find that: (1) LLMs selectively activate task-specific attention heads during SFT; (2) activation patterns for complex tasks are combinations of basic task patterns; and (3) changes in a few parameters can significantly impact activation patterns after SFT on a small number of samples.Based on these insights, experiments are conducted to actually enhance the efficiency and effectiveness of SFT.

Large language models (LLMs) have mastered abundant simple and explicit commonsense knowledge through pre-training, enabling them to achieve human-like performance in simple commonsense reasoning. Nevertheless, LLMs struggle to reason with complex and implicit commonsense knowledge that is derived from simple ones (such as understanding the long-term effects of certain events), an aspect humans tend to focus on more. Existing works focus on complex tasks like math and code, while complex commonsense reasoning remains underexplored due to its uncertainty and lack of structure. To fill this gap and align with real-world concerns, we propose a benchmark Com² focusing on complex commonsense reasoning. We first incorporate causal event graphs to serve as structured complex commonsense. Then we adopt causal theory (e.g., intervention) to modify the causal event graphs and obtain different scenarios that meet human concerns. Finally, an LLM is employed to synthesize examples with slow thinking, which is guided by the logical relationships in the modified causal graphs. Furthermore, we use detective stories to construct a more challenging subset. Experiments show that LLMs struggle in reasoning depth and breadth, while post-training and slow thinking can alleviate this. The code and data are available at https://github.com/Waste-Wood/Com2.

Large language models (LLMs) have shown great potential across various industries due to their remarkable ability to generalize through instruction tuning. However, the limited availability of domain-specific data significantly hampers their performance on specialized tasks. While existing methods primarily focus on selecting training data from general datasets that are similar to the target domain, they often fail to consider the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer. To address these challenges, we introduce G2IS (Gradient-based Graph Instruction Selection), a novel method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions. By accounting for the relationships between instructions, G2IS improves domain adaptation efficiency. Additionally, we propose a gradient walk algorithm to refine the data selection process, enhancing both training effectiveness and efficiency. Our experiments demonstrate that G2IS outperforms traditional methods across various domain adaptation tasks, yielding significant performance gains, particularly in complex, data-scarce scenarios. These results underscore the potential of G2IS in advancing the development of large, domain-specific models.

Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance. However, the impact of each component of the pretraining corpus remains opaque. As a result, the organization of the pretraining corpus is still empirical and may deviate from the optimal. To address this issue, we systematically analyze the impact of 48 datasets from 5 major categories of pretraining data of LLMs and measure their impacts on LLMs using benchmarks about nine major categories of model capabilities. Our analyses provide empirical results about the contribution of multiple corpora on the performances of LLMs, along with their joint impact patterns, including complementary, orthogonal, and correlational relationships. We also identify a set of “high-impact data” such as Books that is significantly related to a set of model capabilities. These findings provide insights into the organization of data to support more efficient pretraining of LLMs.

Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. However, such explanation information still remains absent in existing causal reasoning resources. In this paper, we fill this gap by presenting a human-annotated explainable CAusal REasoning dataset (e-CARE), which contains over 20K causal reasoning questions, together with natural language formed explanations of the causal questions. Experimental results show that generating valid explanations for causal facts still remains especially challenging for the state-of-the-art models, and the explanation information can be helpful for promoting the accuracy and stability of causal reasoning models.