GenAI4ELab/papercli-papers huggingface dataset:
OptScale: Probabilistic Optimality for Inference-time Scaling
Authors: Youkang Wang; Jian Wang; Rubing Chen; Xiao-Yong Wei
Venue: AAAI 2026 (main)

Abstract: Inference-time scaling has emerged as a powerful technique for enhancing the reasoning performance of Large Language Models (LLMs). However, existing approaches often rely on heuristic strategies for parallel sampling, lacking a principled foundation. To address this gap, we propose a probabilistic framework that formalizes the optimality of inference-time scaling under the assumption that parallel samples are independently and identically distributed (i.i.d.), and where the Best-of-N selection strategy follows a probability distribution that can be estimated. Within this framework, we derive a theoretical lower bound on the required number of samples to achieve a target performance level, providing the first principled guidance for compute-efficient scaling. Leveraging this insight, we develop OptScale, a practical algorithm that dynamically determines the optimal number of sampled responses. OptScale employs a language model-based predictor to estimate probabilistic prior parameters, enabling the decision of the minimal number of samples needed that satisfy predefined performance thresholds and confidence levels. Extensive experiments on representative reasoning benchmarks (including MATH-500, GSM8K, AIME, and AMC) demonstrate that OptScale significantly reduces sampling overhead while remaining better or on par with state-of-the-art reasoning performance. Our work offers both a theoretical foundation and a practical solution for principled inference-time scaling, addressing a critical gap in the efficient deployment of LLMs for complex reasoning.
Beyond N-grams: A Hierarchical Reward Learning Framework for Clinically-Aware Medical Report Generation
Authors: Yuan Wang; Shujian Gao; Jiaxiang Liu; Songtao Jiang; Xia Haoxiang; Xiaotian Zhang; Zhaolu Kang; Yemin Wang; Zuozhu Liu
Venue: AAAI 2026 (main)

Abstract: Automatic medical report generation can greatly reduce the workload of doctors, but it is often unreliable for real-world deployment. Current methods can write formally fluent sentences but may be factually flawed, introducing serious medical errors known as clinical hallucinations, which make them untrustworthy for diagnosis. To bridge this gap, we introduce HiMed-RL, a Hierarchical Medical Reward Learning Framework designed to explicitly prioritize clinical quality. HiMed-RL moves beyond simple text matching by deconstructing reward learning into three synergistic levels: it first ensures linguistic fluency at the token-level, then enforces factual grounding at the concept-level by aligning key medical terms with expert knowledge, and finally assesses high-level diagnostic consistency at the semantic-level using a specialized LLM verifier. This hierarchical reward is implemented via a Human-inspired Dynamic Reward Adjustment, a strategy which first teaches the model to learn basic facts before progressing to more complex diagnostic reasoning. Experimentally, HiMed-3B achieves state-of-the-art performance on both in-domain and out-of-domain benchmarks, particularly on the latter, with an improvement of 10.8% over the second-best baseline. Our work provides a robust paradigm for generating reports that not only improve fluency but clinical fine-grained quality.
DualSpeechLM: Towards Unified Speech Understanding and Generation via Dual Speech Token Modeling with Large Language Models
Authors: Yuanyuan Wang; Dongchao Yang; Yiwen Shao; Hangting Chen; Jiankun Zhao; Zhiyong Wu; Helen Meng; Xixin Wu
Venue: AAAI 2026 (main)

Abstract: Extending pre-trained text Large Language Models (LLMs)’s speech understanding or generation abilities by introducing various effective speech tokens has attracted great attention in the speech research community. However, building a unified speech understanding and generation model still faces the following challenges: (1) Due to the huge modality gap between speech and text tokens, extending text LLMs to unified speech LLMs relies on large-scale paired data for fine-tuning, and (2) Generation and understanding tasks prefer information at different levels, e.g., generation benefits from detailed acoustic features, while understanding favors high-level semantics. This divergence leads to difficult performance optimization in one unified model. To solve these challenges, in this paper, we present two key insights in speech tokenization and speech language modeling. Specifically, we first propose an Understanding-driven Speech Tokenizer (USTokenizer), which extracts high-level semantic information essential for accomplishing understanding tasks using text LLMs. In this way, USToken enjoys better modality commonality with text, which reduces the difficulty of modality alignment in adapting text LLMs to speech LLMs. Secondly, we present DualSpeechLM, a dual-token modeling framework that concurrently models USToken as input and acoustic token as output within a unified, end-to-end framework, seamlessly integrating speech understanding and generation capabilities. Furthermore, we propose a novel semantic supervision loss and a Chain-of-Condition (CoC) strategy to stabilize model training and enhance speech generation performance. Experimental results demonstrate that our proposed approach effectively fosters a complementary relationship between understanding and generation tasks, highlighting the promising strategy of mutually enhancing both tasks in one unified model.
BEE-RAG: Balanced Entropy Engineering for Retrieval-Augmented Generation
Authors: Yuhao Wang; Ruiyang Ren; Yucheng Wang; Jing Liu; Xin Zhao; Hua Wu; Haifeng Wang
Venue: AAAI 2026 (main)

Abstract: With the rapid advancement of large language models (LLMs), retrieval-augmented generation (RAG) has emerged as a critical approach to supplement the inherent knowledge limitations of LLMs. However, due to the typically large volume of retrieved information, RAG tends to operate with long context lengths. From the perspective of entropy engineering, we identify unconstrained entropy growth and attention dilution due to long retrieval context as significant factors affecting RAG performance. In this paper, we propose the balanced entropy-engineered RAG (BEE-RAG) framework, which improves the adaptability of RAG systems to varying context lengths through the principle of entropy invariance. By leveraging balanced context entropy to reformulate attention dynamics, BEE-RAG separates attention sensitivity from context length, ensuring a stable entropy level. Building upon this, we introduce a zero-shot inference strategy for multi-importance estimation and a parameter-efficient adaptive fine-tuning mechanism to obtain the optimal balancing factor for different settings. Extensive experiments across multiple RAG tasks demonstrate the effectiveness of BEE-RAG.
Multi-level Style Preference Optimization: An Adaptive Detection Framework for Human-Machine Hybrid Text
Authors: Zehao Wang; Lianwei Wu; Wenbo An; Hang Zhang; Yaxiong Wang
Venue: AAAI 2026 (main)

Abstract: Large language model (LLM) generated texts now rival human quality, creating four text categories: purely machine-generated, machine-rewritten, machine-polished, and human-written content. Traditional detection methods face significant challenges in human-machine hybrid scenarios where LLMs perform rewriting or polishing, as existing approaches focus on single-level features and fail to capture subtle, multi-layered machine traces. To address this, we propose the Multi-level Style Preference Optimization (MSPO) framework, capturing machine style features at multiple granularities: sequence-level (overall consistency), phrase-level (distinctive n-gram patterns), and lexical-level (word selection distributions). We further incorporate four text complexity indicators (Type-Token Ratio, Average Sentence Length, Average Word Length, and Punctuation Ratio) to dynamically adjust optimization parameters based on human-machine text complexity differences, enhancing adaptability across diverse text types. Additionally, we construct a comprehensive detection dataset spanning three representative domains (scientific writing, news articles, and creative writing) across four text types (human-written, purely machine-generated, machine-rewritten, and machine-polished), generated using state-of-the-art LLMs for robust evaluation. Experimental results demonstrate that MSPO significantly outperforms existing methods across all text types. On the challenging rewritten texts, MSPO achieves up to 82.14% AUROC, representing an improvement of 11.15 percentage points over the strongest baseline ImBD, while maintaining robust cross-domain generalizability across scientific, news, and creative writing domains.
CP-Search: A Chain Progressive Search Training Framework Incentivizing the Cognitive Behaviors for Searching in LLMs
Authors: Zehua Wang; Shipeng Li; Buzhou Tang
Venue: AAAI 2026 (main)

Abstract: Retrieval-Augmented Generation (RAG) has been demonstrated to effectively mitigate the knowledge recency issue in Large Language Models (LLMs) while significantly reducing hallucinations. However, existing RAG methods exhibit insufficient capability in modeling reasoning paths for complex multi-hop reasoning tasks. While Reinforcement Learning (RL) has demonstrated success in enhancing model reasoning ability, Token-level RL frameworks exhibit inherent limitations in maintaining coherent reasoning trajectories. This approach remains susceptible to the compounding accumulation of contextual errors during the retrieval process, ultimately resulting in erroneous output generation. To address this challenge, we propose Chain Progressive Search (CP-Search), a novel two-stage training framework designed to enhance the model's retrieval capability in complex scenarios. This framework models the entire retrieval process as a Retrieval-level Markov Decision Process, systematically optimizing the model's retrieval behavior at each step of the chained retrieval. Specifically, CP-Search first constructs a retrieval-cognitive behavioral dataset and employs Supervised Fine-Tuning (SFT) to endow the model with cognitive behaviors for searching. More importantly, by introducing a dense progressive procedural  reward in reinforcement learning training, CP-Search significantly improves the model's reasoning consistency and feedback correction ability in chained retrieval. Experiments conducted on multiple multi-hop datasets demonstrate that CP-Search significantly outperforms existing RAG methods in complex multi-hop reasoning tasks.
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Authors: Zhiyuan Wang; Jinhao Duan; Qingni Wang; Xiaofeng Zhu; Tianlong Chen; Xiaoshuang Shi; Kaidi Xu
Venue: AAAI 2026 (main)

Abstract: Uncertainty quantification (UQ) in foundation models is crucial for identifying and mitigating hallucinations in automatically generated text. 
However, heuristic UQ approaches lack statistical guarantees for key metrics such as the false discovery rate (FDR) in selective prediction tasks. Previous research adopts the split conformal prediction (SCP) framework to ensure desired coverage of admissible answers by constructing data-driven prediction sets, yet these sets typically contain incorrect candidates, undermining their practical effectiveness. To address this, we introduce COIN, an uncertainty-guarding selection framework that calibrates statistically valid uncertainty thresholds to filter a single generated answer per question under user-specified FDR constraints. COIN estimates the empirical error rate on the calibration set and applies confidence interval methods such as Clopper–Pearson to establish a high-probability upper bound on the true error rate (i.e., FDR). This enables the selection of the largest threshold that ensures FDR control on test data while significantly increasing sample retention. We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data across both general and multimodal text generation tasks. Furthermore, we show that employing alternative UQ and upper bound construction strategies can further boost COIN's power performance, which underscores its extensibility and adaptability to diverse application scenarios.
MetaEval: Measuring the Discrimination of Benchmarks for Efficient LLM Evaluation
Authors: Zhuo Wang; Wen Wu; Guoqing Wang; Guangze Ye; Zhenxiao Cheng
Venue: AAAI 2026 (main)

Abstract: Benchmarks serve as standardized test systems to distinguish capabilities among large language models (LLMs). Discriminative items enable high-ability LLMs to favor correct answers, while causing low-ability models to assign lower plausibility to these answers and tend toward incorrect answers. Current methods for assessing benchmark quality primarily focus on coverage of difficulty levels and task diversity, yet lack direct quantification of discrimination—the core metric. Furthermore, large-scale benchmarks incur high evaluation costs. Although heuristic methods can reduce item counts to some extent, they cannot guarantee preservation of the benchmark’s original discriminative properties. To address these limitations, we propose MetaEval, a meta-evaluation framework designed to precisely quantify per-item discrimination and enable efficient assessment. Central to MetaEval is our novel Signal Detection and Item Response (SD-IR) model, which simulates LLMs’ detection of correct answers (signals) by representing each model’s perception through two latent ability states: “known” and “unknown”. For any item, discrimination is quantified as the difference in signal plausibility between these states. Leveraging these discrimination metrics, MetaEval introduces two strategies to replicate full-benchmark results using minimal subsets for efficient evaluation: (1) Distilling metaBench: a compact subset that retains discriminative power by removing redundant items; (2) Predicting performance on full-benchmark based on metaBench’s discrimination. Experiments across five benchmarks confirm that high-discrimination items capture greater performance variation among LLMs, align more closely with full-benchmark rankings, and exhibit superior predictive ability. 
Notably, in the best case, MetaEval achieves accurate full-benchmark estimation using only 2.5% of items, substantially reducing evaluation costs while preserving reliability.
Eliciting Chain-of-Thought in Base LLMs via Gradient-Based Representation Optimization
Authors: Zijian Wang; Yanxiang Ma; Chang Xu
Venue: AAAI 2026 (main)

Abstract: Chain-of-Thought (CoT) reasoning is a critical capability for large language models (LLMs), enabling them to tackle complex multi-step tasks. While base LLMs, pre-trained on general text corpora, often struggle with reasoning due to a lack of specialized training, recent studies reveal their latent reasoning potential tied to hidden states. However, existing hidden state manipulation methods, such as linear activation steering, suffer from limitations due to their rigid and unconstrained nature, often leading to distribution shifts and degraded text quality. In this work, we propose a novel approach for eliciting CoT reasoning from base LLMs through hidden state manipulation grounded in probabilistic conditional generation. By reformulating the challenge as an optimization problem with a balanced likelihood and prior regularization framework, our method guides hidden states toward reasoning-oriented trajectories while preserving linguistic coherence. Extensive evaluations across mathematical, commonsense, and logical reasoning benchmarks demonstrate that our approach consistently outperforms existing steering methods, offering a theoretically principled and effective solution for enhancing reasoning capabilities in base LLMs.
Knowledge Boundary Discovery for Large Language Models
Authors: Ziquan Wang; Zhongqi Lu
Venue: AAAI 2026 (main)

Abstract: We propose Knowledge Boundary Discovery (KBD), a reinforcement learning based framework to explore the knowledge boundaries of the Large Language Models (LLMs). We define the knowledge boundary by automatically generating two types of questions: (i) those the LLM can confidently answer within-knowledge boundary and (ii) those it cannot beyond-knowledge boundary. Iteratively exploring and exploiting the LLM's responses to find its knowledge boundaries is challenging  because of the hallucination phenomenon. To find the knowledge boundaries of an LLM, the agent interacts with the LLM under the modeling of exploring a partially observable environment. The agent generates a progressive question as the action, adopts an entropy reduction as the reward, receives the LLM's response as the observation and updates its belief states. We demonstrate that the KBD detects knowledge boundaries of LLMs by automatically finding a set of non-trivial answerable and unanswerable questions. We validate the KBD by comparing its generated knowledge boundaries with manually crafted LLM benchmark datasets. Experiments show that our KBD-generated question set is comparable to the human-generated datasets. Our approach paves a new way to evaluate LLMs.
SheetBrain: A Neuro-Symbolic Agent for Accurate Reasoning over Complex and Large Spreadsheets
Authors: Ziwei Wang; Jiayuan Su; Mengyu Zhou; Huaxing Zeng; Mengni Jia; Xiao Lv; Haoyu Dong; Xiaojun Ma; Shi Han; Dongmei Zhang
Venue: AAAI 2026 (main)

Abstract: Understanding and reasoning over complex spreadsheets remain fundamental challenges for large language models (LLMs), which often struggle with intricate structures and rely solely on neural computation. In this work, we propose SheetBrain, a neuro-symbolic dual-workflow agent framework for precise and interpretable reasoning over tabular data. SheetBrain consists of an understanding module that produces a comprehensive overview of the spreadsheet, including structural summaries and query-specific analyses to guide execution; an execution module that integrates a Python sandbox with preloaded table-processing libraries and an Excel helper toolkit for effective data manipulation; and a validation module that verifies the correctness of reasoning and answers, triggering re-execution if necessary. We evaluate SheetBrain on multiple public QA and manipulation benchmarks, and introduce SheetBench, a new benchmark targeting large, multi-table, and structurally complex spreadsheets. Experimental results show that SheetBrain significantly improves reasoning performance on both existing benchmarks and the more challenging scenarios presented in SheetBench.
AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale
Authors: Ziyang Wang; Yuanlei Zheng; Zhenbiao Cao; Xiaojin Zhang; Zhongyu Wei; Pei Fu; Zhenbo Luo; Wei Chen; Xiang Bai
Venue: AAAI 2026 (main)

Abstract: For industrial-scale text-to-SQL, supplying the entire database schema to Large Language Models (LLMs) is impractical due to context window limits and irrelevant noise. Schema linking, which filters the schema to a relevant subset, is therefore critical. However, existing methods incur prohibitive costs, struggle to trade off recall and noise, and scale poorly to large databases. We present AutoLink, an autonomous agent framework that reformulates schema linking as an iterative, agent-driven process. Guided by an LLM, AutoLink dynamically explores and expands the linked schema subset, progressively identifying necessary schema components without inputting the full database schema. Our experiments demonstrate AutoLink's superior performance, achieving state-of-the-art strict schema linking recall of 97.4% on Bird-Dev and 91.2% on Spider 2.0-Lite, with competitive execution accuracy, i.e., 68.7% EX on Bird-Dev (better than CHESS) and 34.9% EX on Spider 2.0-Lite (ranking 2nd on the official leaderboard). Crucially, AutoLink exhibits exceptional scalability, maintaining high recall, efficient token consumption, and robust execution accuracy on large schemas (e.g., over 3,000 columns) where existing methods severely degrade—making it a highly scalable, high-recall schema-linking solution for industrial text-to-SQL systems.
MIRAGE: Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains
Authors: Kaiwen Wei; Rui Shan; Dongsheng Zou; Jianzhong Yang; Bi Zhao; Junnan Zhu; Jiang Zhong
Venue: AAAI 2026 (main)

Abstract: Large reasoning models (LRMs) have shown significant progress in test-time scaling through chain-of-thought prompting. 
Current approaches like search-o1 integrate retrieval augmented generation (RAG) into multi-step reasoning processes but rely on a single, linear reasoning path while incorporating unstructured textual information in a flat, context-agnostic manner. As a result, these approaches can lead to error accumulation throughout the reasoning chain, which significantly limits its effectiveness in medical question-answering (QA) tasks where both accuracy and traceability are critical requirements.
To address these challenges, we propose MIRAGE (Multi-path Inference with Retrieval-Augmented Graph Exploration), a novel test-time scalable reasoning framework that performs dynamic multi-path inference over structured medical knowledge graphs. Specifically, MIRAGE 1) decomposes complex queries into entity-grounded sub-questions, 2) executes parallel inference paths, 3) retrieves evidence adaptively via neighbor expansion and multi-hop traversal, and 4) integrates answers using cross-path verification to resolve contradictions. 
Experiments on three medical QA benchmarks (GenMedGPT-5k, CMCQA, and ExplainCPE) show that MIRAGE consistently outperforms GPT-4o, Tree-of-Thought variants, and other retrieval-augmented baselines in both automatic and human evaluations. Additionally, MIRAGE improves interpretability by generating explicit reasoning chains that trace each factual claim to concrete paths within the knowledge graph, making it especially suitable for complex medical reasoning scenarios.
MovieGraph-ToM: Evaluating Long-Range Theory of Mind in Large Language Models via Implicit Social-Causal Graphs
Authors: Tingjiang Wei; Qin Ni; Rong Gao; Yingying Wang; Liang He
Venue: AAAI 2026 (main)

Abstract: The capacity for social reasoning, particularly Theory of Mind (ToM), is a foundational prerequisite for aligning Large Language Models (LLMs) with human values. However, current evaluations are predominantly confined to simplistic, short-text scenarios, obscuring their true capabilities and potential failure modes in complex, long-range social dynamics. To address this deficit, we introduce MovieGraph-ToM, a large-scale benchmark for evaluating long-range ToM and social cognition within extended, multimodal narratives. We employ a "scaffold-and-probe" methodology: we construct a ground-truth Social-Causal Graph offline, which maps the narrative's latent mental states and causal chains. During evaluation, the model is denied access to this graph and must reason directly from raw multimodal inputs. This decoupling forces genuine inference over superficial pattern matching. Reasoning is probed via a hierarchical questioning framework designed to differentiate spontaneous understanding from logical robustness. Our empirical results reveal systematic vulnerabilities in even state-of-the-art models. We identify a critical "multiple-choice pitfall," where accuracy plummets against well-crafted distractors, and a stark "generative-discriminative divide," where models fail to construct coherent explanations for answers they correctly identify. These findings highlight a latent risk, as models that feign comprehension could lead to unpredictable and misaligned behaviors. MovieGraph-ToM thus offers a rigorous platform for assessing and advancing the robust social intelligence required for safely aligned AI systems.