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Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot and temporal evolution across snapshots. However, most existing methods are stateless: they recompute entity representations at each timestamp from a limited query window, leading to episodic amnesia and rapid decay of long-term dependencies. To address this limitation, we propose Entity State Tuning (EST), an encoder-agnostic framework that endows TKG forecasters with persistent and continuously evolving entity states. EST maintains a global state buffer and progressively aligns structural evidence with sequential signals via a closed-loop design. Specifically, a topology-aware state perceiver first injects entity-state priors into structural encoding. Then, a unified temporal context module aggregates the state-enhanced events with a pluggable sequence backbone. Subsequently, a dual-track evolution mechanism writes the updated context back to the global entity state memory, balancing plasticity against stability. Experiments on multiple benchmarks show that EST consistently improves diverse backbones and achieves state-of-the-art performance, highlighting the importance of state persistence for long-horizon TKG forecasting.

ACL 2026

Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting

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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Detecting LLM-generated code is crucial for ensuring software provenance, security, reliability, and licensing compliance. Existing training-free detectors, mostly adapted from text-based methods, rely on global statistics of the Token Perplexity Sequence (TPS) and struggle with code. We reveal a key insight: despite the convergence of global statistics, LLM-generated and human-written code differ fundamentally in their local TPS dynamics: the former shows narrow transient spikes while the latter exhibits broad sustained fluctuations. To capture this distinction, we introduce CodeRipple, a novel training-free detection framework that employs wavelet analysis to characterize TPS morphology across scales. It jointly leverages the Stationary Wavelet Transform to model fluctuation shape and the Discrete Wavelet Transform to quantify cross-scale energy distribution. Evaluated on three challenging benchmarks spanning diverse programming languages, multiple generating LLMs, and various evasion strategies, CodeRipple consistently outperforms existing training-free methods, demonstrating its superior effectiveness and generalizability without any model training. Code available at: https://github.com/yaoxingyu77/CodeRipple.

CodeRipple: Wavelet-Based Detection of LLM-Generated Code

Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal tasks, yet their reliability is persistently undermined by hallucinations—generating text that contradicts visual input. Recent studies often attribute these errors to inadequate visual attention. In this work, we analyze the attention mechanisms via the logit lens, uncovering a distinct anomaly we term **Vocabulary Hijacking**. We discover that specific visual tokens, defined as **Inert Tokens**, disproportionately attract attention. Crucially, when their intermediate hidden states are projected into the vocabulary space, they consistently decode to a fixed set of unrelated words (termed **Hijacking Anchors**) across layers, revealing a rigid semantic collapse. Leveraging this semantic rigidity, we propose **Hijacking Anchor-Based Identification (HABI)**, a robust strategy to accurately localize these Inert Tokens. To quantify the impact of this phenomenon, we introduce the **Non-Hijacked Visual Attention Ratio (NHAR)**, a novel metric designed to identify attention heads that remain resilient to hijacking and are critical for factual accuracy. Building on these insights, we propose **Hijacking-Aware Visual Attention Enhancement (HAVAE)**, a training-free intervention that selectively strengthens the focus of these identified heads on salient visual content. Extensive experiments across multiple benchmarks demonstrate that HAVAE significantly mitigates hallucinations with **no additional computational overhead**, while preserving the model's general capabilities.

Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination

Role-playing agents (RPAs) have made significant strides in mimicking static character identities. However, their personality simulations remain superficial, lacking a profound understanding of complex human psychological mechanisms. We identify a critical bottleneck termed "**Personality Inertia**"—a behavioral rigidity where RLHF-induced alignment bias traps models in a sanitized, "helpful assistant" persona. This inertia prevents models from adapting to diverse social contexts or expressing essential but negative traits under pressure. To bridge this gap, we propose **PD-LLM**, a situation-aware framework grounded in *Trait Activation Theory*. PD-LLM introduces **Bipolar Latent Decomposition**, which decouples personality traits into bidirectional LoRA adapters. These adapters are dynamically modulated by a situation-aware module based on the *DIAMONDS taxonomy*, allowing for precise behavioral regulation. Empirical results show that while baseline methods fail to synchronize multidimensional traits under pressure, PD-LLM achieves superior performance in both **static fidelity** and **dynamic adaptability**. By advancing from prompt engineering to intrinsic parameter control, PD-LLM effectively overcomes personality rigidity, facilitating the creation of vivid and psychologically consistent agents.

Trait Activation in Silicon: A Situation-Aware Framework for Psychologically Grounded Role-Playing

As applications of large language models (LLMs) become increasingly complex, the demand for robust complex instruction following capabilities is growing accordingly. We argue that a thorough understanding of the instruction itself, especially the latent reasoning structure embedded between the lines, is crucial for improving instruction following. Therefore we target complex instructions that involve implicit reasoning, intricate logical relations, and multi-constraint dependencies. We propose ImpRIF, a method to enhance LLMs' understanding of implicit reasoning instructions, thereby improving its ability to follow complex instructions. We formalize such instructions as verifiable reasoning graphs, enabling programmatic verification and graph-driven chain-of-thought reasoning. Based on this formulation, we synthesize large-scale single- and multi-turn data, propose fine-tuning with graph reasoning, and apply reinforcement learning to explicitly train models to reason along the graph. On five complex instruction following benchmarks, our models substantially outperform their base models. These results demonstrate that enhancing implicit reasoning capabilities can significantly improve complex instruction following. This project will be open-sourced in the near future.

ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following

Large Audio-Language Models (LALMs) have recently achieved strong performance across various audio-centric tasks. However, hallucination, where models generate responses that are semantically incorrect or acoustically unsupported, remains largely underexplored in the audio domain. Existing hallucination benchmarks mainly focus on text or vision, while the few audio-oriented studies are limited in scale, modality coverage, and diagnostic depth. We therefore introduce HalluAudio, the first large-scale benchmark for evaluating hallucinations across speech, environmental sound, and music. HalluAudio comprises over 5K human-verified QA pairs and spans diverse task types, including binary judgments, multi-choice reasoning, attribute verification, and open-ended QA. To systematically induce hallucinations, we design adversarial prompts and mixed-audio conditions. Beyond accuracy, our evaluation protocol measures hallucination rate, yes/no bias, error-type analysis, and refusal rate, enabling a fine-grained analysis of LALM failure modes. We benchmark a broad range of open-source and proprietary models, providing the first large-scale comparison across speech, sound, and music. Our results reveal significant deficiencies in acoustic grounding, temporal reasoning, and music attribute understanding, underscoring the need for reliable and robust LALMs.

HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models

Slow-thinking Large Language Models (LLMs) have demonstrated strong reasoning capabilities but often suffer from severe hallucinations due to an inability to recognize their knowledge boundaries. Existing Reinforcement Learning (RL) approaches typically rely on outcome-oriented rewards, which can inadvertently reinforce fabricated reasoning paths when the final answer is correct. To address this, we propose **Know**ledge-enhanced **RL**, **KnowRL**, a framework that integrates factual supervision directly into the reasoning process. By decomposing the chain of thought into atomic facts and verifying them against the corresponding ground-truth knowledge, KnowRL performs fine-grained checks to encourage models to reason faithfully. Crucially, this process-oriented supervision teaches the model to identify its knowledge boundaries, learning to say "I don't know" instead of fabricating answers when information is missing. Experimental results demonstrate that KnowRL effectively mitigates hallucinations---reducing the Incorrect Rate on SimpleQA by 20.3% for distillation-based slow-thinking models while maintaining strong performance on complex reasoning benchmarks like GPQA and AIME 2025. Furthermore, our method shows robust transferability to out-of-distribution tasks, indicating that the model learns a generalizable verification behavior.

KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality

The ability of large language models (LLMs) to manage and acquire economic resources remains unclear. In this paper, we introduce \textbf{Market-Bench}, a comprehensive benchmark that evaluates the capabilities of LLMs in economically-relevant tasks through economic and trade competition. Specifically, we construct a configurable multi-agent supply chain economic model where LLMs act as retailer agents responsible for procuring and retailing merchandise. In the \textbf{procurement} stage, LLMs bid for limited inventory in budget-constrained auctions. In the \textbf{retail} stage, LLMs set retail prices, generate marketing slogans, and provide them to buyers through a role-based attention mechanism for purchase. Market-Bench logs complete trajectories of bids, prices, slogans, sales, and balance-sheet states, enabling automatic evaluation with economic, operational, and semantic metrics. Benchmarking on 20 open- and closed-source LLM agents reveals significant performance disparities and winner-take-most phenomenon, \textit{i.e.}, only a small subset of LLM retailers can consistently achieve capital appreciation, while many hover around the break-even point despite similar semantic matching scores. Market-Bench provides a reproducible testbed for studying how LLMs interact in competitive markets.

Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition

Retrieval-Augmented Generation (RAG) has been proposed to mitigate hallucinations in large language models (LLMs), where generated outputs may be factually incorrect. However, existing RAG approaches predominantly rely on vector similarity for retrieval, which is prone to semantic noise and fails to ensure that generated responses fully satisfy the complex conditions specified by factual queries, often leading to incorrect answers. To address this challenge, we introduce a novel research problem, named Exact Retrieval Problem (ERP). To the best of our knowledge, this is the first problem formulation that explicitly incorporates structural information into RAG for factual questions to satisfy all query conditions. For this novel problem, we propose Structure Guided Retrieval-Augmented Generation (SG-RAG), which models the retrieval process as an embedding-based subgraph matching task, and uses the retrieved topological structures to guide the LLM to generate answers that meet all specified query conditions. To facilitate evaluation of ERP, we construct and publicly release Exact Retrieval Question Answering (ERQA), a large-scale dataset comprising $120{,}000$ fact-oriented QA pairs, each involving complex conditions, spanning $20$ diverse domains. The experimental results demonstrate that SG-RAG significantly outperforms strong baselines on ERQA, delivering absolute improvements from $20.68$ to $50.88$ points across all evaluation metrics, while maintaining reasonable computational overhead.

Structure Guided Retrieval-Augmented Generation for Factual Queries

Multimodal large language models (MLLMs) have achieved remarkable progress in recent years, yet their ability to perform left–right reasoning in mirror contexts—a fundamental element of spatial cognition—remains underexplored. To address this gap, we introduce MirrorQA, a manually constructed benchmark with 5,549 samples, designed to evaluate MLLMs’ capability to distinguish left from right from a subject-centered perspective. MirrorQA is built through a three-stage pipeline (annotation, verification, and final review) to ensure high-quality labeling. Comprehensive evaluations on both open- and closed-source MLLMs show that even the best-performing models achieve only 65.40% accuracy, far below the 99.28% accuracy of humans. These results highlight substantial challenges in current MLLMs when reasoning about left and right, and point to promising directions for future research. MirrorQA and its code are publicly available at anonymous link https://github.com/stargazer-zeno/MirrorQA.

MirrorQA: Benchmarking Multimodal LLMs on Mirror-Orientation Reasoning

Brain-tuning enhances brain alignment and downstream performance by fine-tuning speech language models with neural recordings. However, previous work relies primarily on fMRI, whose temporal resolution integrates neural activity over seconds, blending distinct processing stages into a single supervision signal and precluding temporally targeted training. We introduce ECoG-tuning, which leverages electrocorticography's millisecond precision to train speech language models. We design temporally targeted windows—a speech window capturing acoustic-phonetic encoding and a language window capturing higher-order linguistic processing—grounded in neuroscientific findings about temporal encoding hierarchies. Evaluating three models on the Podcast ECoG dataset, we find that ECoG-tuning significantly improves brain alignment over pretrained and distillation baselines. Notably, full spatiotemporal dynamics yield 7–17% higher alignment than time-averaged supervision across models, and language-window tuning produces larger gains in higher-order language regions, indicating that temporal precision provides additional training value. Moreover, ECoG-tuned models consistently improve or maintain downstream performance. Overall, our work provides initial evidence that electrophysiology is a viable brain-tuning modality, demonstrating how neuroscientific insights into processing hierarchies can inform principled model training strategies. Code is available at [https://github.com/Mochizuki-BUPT/ECoG-Tuning-main](https://github.com/Mochizuki-BUPT/ECoG-Tuning-main).

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