Due to the dynamically evolving nature of real-world query streams, relevance models struggle to generalize to practical search scenarios. A sophisticated solution is self-evolution techniques. However, in large-scale industrial settings with massive query streams, this technique faces two challenges: (1) informative samples are often sparse and difficult to identify, and (2) pseudo-labels generated by the current model could be unreliable. To address these challenges, in this work, we propose a Self-Evolving Relevance Model approach (SERM), which comprises two complementary multi-agent modules: a multi-agent sample miner, designed to detect distributional shifts and identify informative training samples, and a multi-agent relevance annotator, which provides reliable labels through a two-level agreement framework. We evaluated SERM on a large-scale industrial platform, which serves billions of user requests daily. Experimental results demonstrate that SERM can achieve significant performance gains through iterative self-evolution, as validated by extensive offline multilingual evaluations and online testing.
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Large language models (LLMs) enable zero-shot and few-shot multi-label text classification via in-context learning, yet most approaches perform static inference and degrade under streaming test data due to distribution shift and long-tail labels. We study online test-time adaptation for LLM-based multi-label generation without any parameter updates, and identify two bottlenecks: (1) standard generation probabilities provide unreliable confidence because they ignore label competition at key decoding branches; (2) naive confidence-based caching overfits to frequent and easy examples, reducing label coverage and diversity. We propose SCOTTA, a structured confidence-guided online adaptation framework. SCOTTA introduces Label-set Local Likelihood Ratio (L3R), a label-level confidence measure that compares a target label against its valid competitors at critical decision positions. Using L3R as a unified signal, SCOTTA maintains an in-context exemplar cache via streaming submodular maximization, balancing label coverage, semantic diversity, and sample quality under a fixed context budget. Across four benchmarks, SCOTTA consistently improves Micro-F1 and Macro-F1 over strong LLM and non-LLM baselines, with the largest gains on long-tail labels.
Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thought (CoT) have further elevated their performance in handling complex logical problems. Despite these advances, high-quality reasoning remains heavily reliant on manual static prompts and is sensitive to decoding configurations and task distributions, leading to performance fluctuations and limited transferability. Existing automatic prompt optimization methods typically adopt single-agent local search, failing to simultaneously optimize prompts and decoding hyperparameters within a unified framework to achieve stable global improvements. To address this limitation, we propose Agent-GWO, a dynamic prompt optimization framework for complex reasoning. Specifically, we unify prompt templates and decoding hyperparameters as inheritable agent configurations. By leveraging the leader-follower mechanism of the Grey Wolf Optimizer (GWO), we automatically select three leader agents (\alpha, \beta, and \delta) to guide the collaborative updates of the remaining agents, enabling iterative convergence toward robust optimal reasoning configurations that can be seamlessly integrated for inference. Extensive experiments on multiple mathematical and hybrid reasoning benchmarks across diverse LLM backbones show that Agent-GWO consistently improves accuracy and stability over existing prompt optimization methods.
Recent advancements in audio diffusion models have significantly improved text-to-audio editing via inversion techniques. However, these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity during inversion and generation, leading to prohibitive computational costs. We propose AdaTE, a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates the inversion-based editing process by dynamically evaluating only the most critical generative phases. Specifically, we introduce a hierarchical probing mechanism that monitors curvature acceleration and information gain to detect pivotal transitions within the latent flow. This allows the model to selectively skip redundant segments via linear extrapolation while preserving dense neural evaluations for complex semantic changes. Extensive experiments across AudioLDM2, Auffusion, and Tango2 demonstrate that AdaTE achieves up to a 3.9\times speedup with negligible loss in fidelity. AdaTE significantly shifts the Pareto frontier, providing an efficient solution for high-fidelity audio synthesis and editing.
LLMs struggle with Semantic Inertia: the inability to inhibit pre-trained priors (e.g., “Lava is Dangerous”) when dynamic, in-context rules contradict them. We probe this phenomenon using , where physical laws are mutable text rules, enabling precise evaluation of models’ ability to override learned priors when rules change. We quantatively observe that larger models can exhibit inverse scaling: they perform worse than smaller models when natural language reasoning requires suppressing pre-trained associations (e.g., accepting “Lava is Safe”). Our analysis attributes this to natural language encoding, which entangles descriptive semantics and logical rules, leading to persistent hallucinations of familiar physics despite explicit contradictory rules. Here we show that representing dynamics as executable code, rather than descriptive text, reverses this trend and enables effective prior inhibition. We introduce LCV, which fine-tunes models on counterfactual pairs and identifies states with contradictory rules, thereby forcing attention to logical constraints rather than visual semantics. This training-time approach outperforms expensive inference-time search methods in both efficiency and accuracy. Our results demonstrate that representation fundamentally determines whether scaling improves or impairs contextual reasoning. This challenges the assumption that larger models are universally better, with implications for domains that require dynamic overriding of learned priors.
Psychological Counseling Cannot Be Achieved Overnight: Automated Psychological Counseling Through Multi-Session Conversations
PDF ↗In recent years, Large Language Models (LLMs) have made significant progress in automated psychological counseling. However, current research focuses on single-session counseling, which doesn’t represent real-world scenarios. In practice, psychological counseling is a process, not a one-time event, requiring sustained, multi-session engagement to progressively address clients’ issues. To overcome this limitation, we introduce a dataset for Multi-Session Psychological Counseling Conversation Dataset (Muspsy). Our Muspsy dataset is constructed using real client profiles from publicly available psychological case reports. It captures the dynamic arc of counseling, encompassing multiple progressive counseling conversations from the same client across different sessions. Leveraging our dataset, we also developed our Muspsy model, which aims to track client progress and adapt its counseling direction over time. Experiments show that our model performs better than baseline models across multiple sessions.
While large language models exhibit strong reasoning capabilities, prior work shows that their performance can be further enhanced by encouraging greater exploration. However, existing approaches overlook the presence of unhealthy exploration that increases exploration-related token usage without contributing to effective problem-solving. In this work, we show that prompt ambiguity can artificially prolong early-stage exploration, manifested as an elevated and delayed early-stage entropy peak. Although this uncertainty may be gradually resolved as reasoning progresses, reflected in the eventual convergence of the late-stage entropy peak, it does not meaningfully improve accuracy or self-consistency and instead substantially reduces reasoning efficiency. Motivated by these observations, we propose an entropy-dynamics-aware prompt optimization framework that trains a lightweight optimizer to generate concise clarifications. These clarifications aim to reduce ambiguity-induced early-stage uncertainty while preserving the model’s reasoning capabilities. Extensive experiments across multiple models, reasoning budgets, and benchmarks demonstrate that our approach consistently improves reasoning efficiency by up to 52%, by reducing unhealthy exploration without sacrificing accuracy.
Large Language Models (LLMs) encode vast factual knowledge, yet their inability to selectively forget specific information hinders privacy protection, bias mitigation, and post-deployment correction. We present LoRA-based Unlearning with Negative Examples (LUNE), a lightweight framework that performs negative-only unlearning by updating only low-rank adapters while freezing the backbone, thereby localizing edits and avoiding disruptive global changes. Leveraging Low-Rank Adaptation (LoRA), LUNE targets intermediate representations to suppress (or replace) requested knowledge with an order-of-magnitude lower compute and memory than full fine-tuning or direct weight editing. Extensive experiments on multiple factual unlearning tasks show that LUNE: (I) achieves effectiveness comparable to full fine-tuning and memory-editing methods; and (II) reduces computational cost by about an order of magnitude.
DiSec: Mitigating Backdoors in Pre-trained Language Models via Disentanglement of Adversarial Weights for Secure Fine-Tuning
PDF ↗Task-agnostic backdoor attacks can contaminate pre-trained language models (PLMs) in a way that survives downstream adaptation, even under full fine-tuning, making it difficult for practitioners to trust third-party checkpoints. Existing defenses often rely on privileged assumptions (e.g., access to poisoned data or trigger/target knowledge), thereby limiting their applicability in realistic settings. We present DiSec, a robust and label-efficient purification framework that uses only clean auxiliary text and does not rely on downstream supervision or attack signatures. DiSec elicits model-internal signals from this clean data to separate suspicious parameter components that are inconsistent with benign behavior, and then flags anomalous structures by jointly leveraging complementary spectral and generative views of outliers. Finally, DiSec performs a structure-preserving repair via layer-local prototype-based mean correction, yielding an idempotent update that depends only on non-adversarial statistics. Across diverse downstream classification tasks and PLM backdoor strategies, DiSec substantially suppresses attack success while preserving clean-task utility, offering a practical path to securing fully fine-tuned PLMs before deployment. The codes are publicly available at https://github.com/das-sunanda/DiSec.
Mid-training has become critical for enhancing the knowledge and reasoning ability of large language models (LLMs), especially through the utilization of large-scale synthetic data. However, existing data synthesis methods often generate simplistic and homogeneous QA pairs, with limited scale and diversity. To address this, we propose BoostQA, a novel framework designed to synthesize large-scale, diverse, and high-quality QA data for mid-training. BoostQA introduces model probes during mid-training for the first time and implements STEM-focused multi-grade synthesis to boost data diversity as well as high-difficulty synthesis to alleviate difficulty degradation, followed by answer refinement to further improve quality. Extensive experiments by mid-training Llama-3 8B demonstrate that using only 20B-token BoostQA data achieves a significant average improvement of **12.74%** on MMLU and CMMLU over the pre-training baseline. After mid-training on 500B tokens, including 100B-token BoostQA data, our model achieves SOTA average results across benchmarks among mainstream models of comparable size. BoostQA also demonstrates robust scalability, with performance consistently improving as model size, data volume, and initial FLOPs scale.
CaM-HG: Causal-Enhanced MoE and Hypergraphs Network for Incomplete Multimodal Emotion Recognition in Conversations
PDF ↗Multimodal Emotion Recognition in Conversation (MERC) relies on integrating heterogeneous signals, yet real-world modality missingness frequently disrupts these systems. We contend that missingness is not merely a loss of data fidelity but a rupture of the fine-grained inter-modal causal chains essential for reasoning. Existing methods, which primarily focus on statistical reconstruction, often fail to bridge these logical gaps, effectively leaving semantic holes. To address this, we propose the Causal-Enhanced Mixture-of-Experts and Hypergraph Network (CaM-HG), employing a "restore-then-mine" paradigm. First, a Causal-Enhanced MoE module conditions experts on historical context to synthesize missing features that are both realistic and causally consistent, thereby patching the broken topology. Subsequently, an Asymmetric Causal Dynamic Hypergraph mines high-order correlations from the restored graph while enforcing strict temporal causality. Experiments on IEMOCAP, CMU-MOSI, and CMU-MOSEI show consistent improvements in terms of WAF1 and accuracy over strong baselines, e.g., surpassing SOTA benchmarks by 1.43% and 1.25% on IEMOCAP. The source code is included in the supplementary material.
QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation
PDF ↗Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models (LLMs). However, existing methods rely on model-internal signals (e.g., logits, entropy), which are fundamentally unreliable because LLMs are typically ill-calibrated and often exhibit high confidence in erroneous outputs. We propose QuCo-RAG, which shifts from subjective confidence to objective statistics computed from pre-training data. Our method quantifies uncertainty through two stages: (1) before generation, we identify low-frequency entities indicating long-tail knowledge gaps; (2) during generation, we verify entity co-occurrence in the pre-training corpus, where zero co-occurrence often signals hallucination risk. Both stages leverage Infini-gram for millisecond-latency queries over 4 trillion tokens, triggering retrieval when uncertainty is high. Experiments on multi-hop QA benchmarks show QuCo-RAG achieves EM gains of 5–12 points over state-of-the-art baselines with OLMo-2 models, and transfers effectively to models with undisclosed pre-training data (Llama-3, Qwen2.5, GPT-4.1/5-chat), improving EM by up to 14 points. Generalization to long-form generation and biomedical QA further validates the robustness of our paradigm. These results establish corpus-grounded verification as a principled, practically model-agnostic paradigm for dynamic RAG.
Large language models (LLMs) achieve remarkable performance on diverse downstream and domain-specific tasks via parameter-efficient fine-tuning (PEFT). However, existing PEFT methods, particularly MoE-LoRA architectures, suffer from limited parameter efficiency and coarse-grained adaptation due to the proliferation of LoRA experts and instance-level routing. To address these issues, we propose Core Space Mixture of LoRA (\textbf{CoMoL}), a novel MoE-LoRA framework that incorporates expert diversity, parameter efficiency, and fine-grained adaptation. Specifically, CoMoL introduces two key components: core space experts and core space routing. Core space experts store each expert in a compact core matrix, preserving diversity while controlling parameter growth. Core space routing dynamically selects and activates the appropriate core experts for each token, enabling fine-grained, input-adaptive routing. Activated core experts are then merged via a soft-merging strategy into a single core expert, which is combined with a shared LoRA to form a specialized LoRA module. Besides, the routing network is projected into the same low-rank space as the LoRA matrices, further reducing parameter overhead without compromising expressiveness. Extensive experiments demonstrate that CoMoL retains the adaptability of MoE-LoRA architectures while achieving parameter efficiency comparable to standard LoRA, consistently outperforming existing methods across multiple tasks. Our code is available at https://github.com/DCDmllm/CoMoL.
Large Language Model agents deployed in complex environments frequently encounter a conflict between maximizing goal achievement and adhering to safety constraints. This paper identifies a new concept called Agentic Pressure, which characterizes the endogenous tension emerging when compliant execution becomes infeasible. We demonstrate that under this pressure agents exhibit normative drift where they strategically sacrifice safety to preserve utility. Notably we find that advanced reasoning capabilities accelerate this decline as models construct linguistic rationalizations to justify violation. Finally, we analyze the root causes and explore preliminary mitigation strategies, such as pressure isolation, which attempts to restore alignment by decoupling decision-making from pressure signals.
The paradigm of programmable diagram generation is evolving rapidly, playing a crucial role in structured visualization. However, most existing studies are confined to a narrow range of task formulations and language support, constraining their applicability to diverse diagram types. In this work, we propose OmniDiagram, a unified framework that incorporates diverse diagram code languages and task definitions. To address the challenge of aligning code logic with visual fidelity in Reinforcement Learning (RL), we introduce a novel visual feedback strategy named Visual Interrogation Verifies All (Viva). Unlike brittle syntax-based rules or pixel-level matching, Viva rewards the visual structure of rendered diagrams through a generative approach. Specifically, Viva actively generates targeted visual inquiries to scrutinize diagram visual fidelity and provides fine-grained feedback for optimization. This mechanism facilitates a self-evolving training process, effectively obviating the need for manually annotated ground truth code. Furthermore, we construct M3^2Diagram, the first large-scale diagram code generation dataset, containing over 196k high-quality instances. Experimental results confirm that the combination of SFT and our Viva-based RL allows OmniDiagram to establish a new state-of-the-art (SOTA) across diagram code generation benchmarks.
Causal reasoning is a crucial component of understanding complex phenomena and building intelligent systems. Recent advancements in large language models (LLMs) have demonstrated their strong capabilities in reasoning tasks; however, their true understanding of causal relationships remains limited, particularly in cases where causal chains are misidentified or reliance on empirical inference occurs. To mitigate the risk that models misclassify data as false positives due to these issues, we introduce CausalityCheck, an automated tool designed to efficiently generate causal reasoning checklists. This checklist enables the creation of multi-task causal reasoning datasets with task generalization and reasoning robustness from a single causal reasoning dataset. Using CausalityCheck, we developed CausalityCheck-CP to assess the causal reasoning abilities of 18 LLMs. This framework also measures the extent to which causal chains are misidentified or rely on empirical inferences. Our results indicate that the current large language models still face two critical issues when handling complex causal reasoning tasks: incorrect identification of causal chains and reliance on empirical inference. The code and data are available at https://github.com/dzh597/CausalityCheck.
The emergence of reasoning models, exemplified by OpenAI o1, signifies a transition from intuitive to deliberative cognition, effectively reorienting the scaling laws from pre-training paradigms toward test-time computation. While Monte Carlo Tree Search (MCTS) has shown promise in this domain, existing approaches typically treat each rollout as an isolated trajectory. This lack of information sharing leads to severe inefficiency and substantial computational redundancy, as the search process fails to leverage insights from prior explorations. To address these limitations, we propose PRISM-MCTS, a novel reasoning framework that draws inspiration from human parallel thinking and reflective processes. PRISM-MCTS integrates a Process Reward Model (PRM) with a dynamic shared memory, capturing both "Heuristics" and "Fallacies". By reinforcing successful strategies and pruning error-prone branches, PRISM-MCTS effectively achieves refinement. Furthermore, we develop a data-efficient training strategy for the PRM, achieving high-fidelity evaluation under a few-shot regime. Empirical evaluations across diverse reasoning benchmarks substantiate the efficacy of PRISM-MCTS. Notably, it halves the trajectory requirements on GPQA while surpassing MCTS-RAG and Search-o1, demonstrating that it scales inference by reasoning judiciously rather than exhaustively.
Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction
PDF ↗Aspect Sentiment Quad Prediction (ASQP) is a fundamental yet challenging task in fine-grained sentiment analysis, particularly when aspects or opinions are implicit. Existing methods often lack explainability and generalization, making it difficult to justify inference decisions and to detect implicit sentiment across domains and varied expression patterns. To address these limitations, we propose Tree-CoT-RT, an explainable multi-path tree-guided chain-of-thought and reinforcement learning framework specifically designed for ASQP. The core idea is to use sentiment tree structures to design type-specific reasoning templates that guide LLMs in generating explainable chains, including both final sentiment quadruples and intermediate inference steps for transparent implicit reasoning. However, the generated reasoning chains often vary in quality and may contain logical inconsistencies. To mitigate this, we introduce a reinforcement learning strategy with a rule-based reward function to generate high-quality reasoning traces, which are then used to fine-tune the LLM and enable controlled sampling. Experiments on benchmark datasets demonstrate that Tree-CoT-RT substantially outperforms strong baselines, particularly in scenarios involving implicit sentiment analysis.
mPresenter: An Agentic Framework for Generating Multilingual Presentation Videos from Scientific Papers
PDF ↗Generating presentation videos from scientific papers is challenging due to the need for long-document discourse planning and cross-lingual grounding. Existing Paper2Video systems are largely monolingual and often rely on single-pass pipelines, which can limit the coherence and informativeness of the resulting presentations.We present mPresenter, a multilingual agentic Paper2Video system that decomposes the task into planning, audience-oriented critique, layout-aware slide generation, and multilingual figure interpretation, enabling iterative refinement at the discourse level. To facilitate reproducible evaluation, we also introduce mPreBench, a multilingual benchmark that evaluates presentation videos via question answering as a proxy for effective information transfer. Experimental results indicate that mPresenter improves question-answering accuracy relative to prior systems, while maintaining affordable cost and latency.
Learning Continuous Temporal Dynamics on Symplectic Manifolds for Temporal Knowledge Graph Embedding
PDF ↗Temporal knowledge graph embedding (TKGE) aims to model the temporal evolution of relational facts. However, existing approaches predominantly rely on discrete timestamp lookup tables and high-dimensional embedding spaces, which lack explicit structural constraints for continuous-time dynamics. As a result, temporal patterns are often captured through capacity scaling rather than principled dynamic modeling, leading to limited parameter efficiency and scalability.To address these limitations, we propose , a physics-inspired framework that embeds temporal dynamics into a symplectic phase space. Our model introduces a structure-preserving Hamiltonian evolution mechanism based on a pairwise-decoupled Hamiltonian generator and its Cayley transform, ensuring that temporal transformations adhere to the symplectic group \mathrm{Sp}(2d) and preserve phase-space volume with linear computational complexity. In addition, we design a Time-Aware Parameter Modulation mechanism that integrates continuous Rotary Time Embeddings via Feature-wise Linear Modulation, enabling smooth temporal evolution while capturing event-driven variations. Theoretical analysis establishes the geometric validity of the proposed framework. Extensive experiments on standard TKGE benchmarks demonstrate that achieves competitive performance with substantially lower embedding dimensions. Furthermore, empirical results show that the proposed continuous Hamiltonian evolution facilitates generalization to unseen timestamps by learning transferable temporal dynamics from the underlying geometric structure.