GenAI4ELab/papercli-papers huggingface dataset:
Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces
Authors: Shreyas Rajesh; Pavan Holur; Chenda Duan; David Chong; Vwani Roychowdhury
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) face fundamental challenges in long-context reasoning: many documents exceed their finite context windows, while performance on texts that do fit degrades with sequence length, necessitating their augmentation with external memory frameworks. Current solutions, which have evolved from retrieval using semantic embeddings to more sophisticated structured knowledge graphs representations for improved sense-making and associativity, are tailored for fact-based retrieval and fail to build the space-time-anchored narrative representations required for tracking entities through episodic events. To bridge this gap, we propose the Generative Semantic Workspace (GSW), a neuro-inspired generative memory framework that builds structured, interpretable representations of evolving situations, enabling LLMs to reason over evolving roles, actions, and spatiotemporal contexts. Our framework comprises an Operator, which maps incoming observations to intermediate semantic structures, and a Reconciler, which integrates these into a persistent workspace that enforces temporal, spatial, and logical coherence. On the Episodic Memory Benchmark (EpBench) comprising corpora ranging from 100k to 1M tokens in length, GSW outperforms existing RAG based baselines by up to 20%. Furthermore, GSW is highly efficient, reducing query-time context tokens by 51% compared to the next most token-efficient baseline, reducing inference time costs considerably. More broadly, GSW offers a concrete blueprint for endowing LLMs with human-like episodic memory, paving the way for more capable agents that can reason over long horizons.
Are Language Models Any Good at Density Modeling?
Authors: Sriram Ranga; Sai Shashank Bedampeta; Rui Mao; Anupam Chattopadhyay
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) surprised the world with their ability to mimic humans in writing and are starting to be used as simulations of human writers for various kinds of linguistic analyses. However, these analyses rest on the belief that LLMs are good density models that accurately capture the underlying probability distribution of the language. In this paper, we question this basic assumption and try to evaluate language models on their density modelling capabilities. Since a ground truth does not exist for the probability distribution of any natural language, we come up with a synthetic language made up of decimal numbers written in words in English. We train language models from scratch on various probability distributions over this synthetic language and compare the distributions learned by the models with the original distributions. Experiments show that language models can learn underlying probability distributions across a wide range of cases, but they fail when those distributions depend on deep semantic properties of numbers that cannot be inferred from syntactic patterns. Additionally, we observed a strong bias in the models towards numbers that frequently occur as substrings within other numbers. This suggests that such a bias possibly exists in real-world natural language models as well, and negatively impacts downstream tasks and analyses that rely on model-generated probabilities.
A Text-Routed Sparse Mixture-of-Experts Model with Explanation and Temporal Alignment for Multi-Modal Sentiment Analysis
Authors: Dongning Rao; Yunbiao Zeng; Zhihua Jiang; Jujian Lv
Venue: AAAI 2026 (main)

Abstract: Human-interaction-involved applications underscore the need for Multi-modal Sentiment Analysis (MSA). Although many approaches have been proposed to address the subtle emotions in different modalities, the power of explanations and temporal alignments is still underexplored. Thus, this paper proposes the Text-routed sparse mixture-of-Experts model with eXplanation and Temporal alignment for MSA (TEXT). TEXT first augments explanations for MSA via Multi-modal Large Language Models (MLLM), and then novelly aligns the representations of audio and video through a temporality-oriented neural network block. TEXT aligns different modalities with explanations and facilitates a new text-routed sparse mixture-of-experts with gate fusion. Our temporal alignment block merges the benefits of Mamba and temporal cross-attention. As a result, TEXT achieves the best performance across four datasets among all tested models, including three recently proposed approaches and three MLLMs. TEXT wins on at least four metrics out of all six metrics. For example, TEXT decreases the mean absolute error to 0.353 on the CH-SIMS dataset, which signifies a 13.5% decrement compared with recently proposed approaches.
SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling
Authors: Md Imbesat Hassan Rizvi; Xiaodan Zhu; Iryna Gurevych
Venue: AAAI 2026 (main)

Abstract: Process or step-wise supervision has played a crucial role in advancing complex multi-step reasoning capabilities of Large Language Models (LLMs). However, efficient, high-quality automated process annotation remains a significant challenge.  To address this, we introduce Single-Pass Annotation with Reference-Guided Evaluation (SPARE), a novel structured framework that enables efficient per-step annotation by jointly aligning solution steps to reference solutions and determine its accuracy with explicit reasoning in single generation. We demonstrate SPARE's effectiveness across four diverse datasets spanning mathematical reasoning (GSM8K, MATH), multi-hop question answering (MuSiQue-Ans), and spatial reasoning (SpaRP), showing consistent improvements in two applications: (1) training Process Reward Models (PRMs) for ranking and aggregating multiple generations, and (2) fine-tuning models via offline reinforcement learning for greedy decoding. On PROCESSBENCH, SPARE demonstrates data-efficient out-of-distribution generalization, using only ~16% of training samples compared to human-labeled and other synthetically trained baselines. Additionally, it achieves competitive performance with MCTS-based methods while offering 2.3x speedup in terms of total token count. Manual analysis reveals complementary precision-recall characteristics with MCTS approaches, suggesting potential for ensemble methods. These results establish SPARE as a practical and scalable solution for automatic process supervision in LLM reasoning.
Anchor Watermark: Robust Attribution for Diffusion-based Text-to-Audio Model
Authors: Xianjin Rong; Donghui Hu
Venue: AAAI 2026 (main)

Abstract: With the increasing commercialization of the latent diffusion-based text-to-audio  generation, model attribution has become a critical challenge. Embedding watermarks in generated audio is an effective way to distinguish synthetic from natural audio. However, existing watermarking methods often suffer from limited robustness or require additional training, limiting their scalability in practical applications. In this paper, we propose an anchor-based inversion optimization framework. The method embeds a watermark into the model's initial latent vector, designated as a pivotal anchor, and extracts the watermark through inversion. To mitigate error accumulation and enhance robustness during inversion, we leverage the temporal consistency and distributional similarity of diffusion models, formulating watermark extraction as a time-series optimization problem. Specifically, given a suspicious audio sample and a candidate model with a predefined anchor, we first perform unguided denoising diffusion on the anchor to generate an intermediate latent trajectory as the anchor sequence. Then, we optimize the inversion process to align the inverted trajectory with the anchor sequence, thereby reducing accumulated errors. During optimization, we adopt Soft Dynamic Time Warping as the loss function. Its flexible temporal alignment capability ensures that correct attribution is achieved only when the anchor matches the target audio. Experimental results show that our method enables training-free attribution while preserving audio quality and achieving strong robustness.
PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement
Authors: Xiaobin Rong; Qinwen Hu; Mansur Yesilbursa; Kamil Wojcicki; Jing Lu
Venue: AAAI 2026 (main)

Abstract: Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative approaches. However, existing generative SE approaches often overlook the risk of hallucination under severe noise, leading to incorrect spoken content or inconsistent speaker characteristics, which we term linguistic and acoustic hallucinations, respectively. We argue that linguistic hallucination stems from models' failure to constrain valid phonological structures and it is a more fundamental challenge. While language models (LMs) are well-suited for capturing the underlying speech structure through modeling the distribution of discrete tokens, existing approaches are limited in learning from noise-corrupted representations, which can lead to contaminated priors and hallucinations. To overcome these limitations, we propose the Phonologically Anchored Speech Enhancer (PASE), a generative SE framework that leverages the robust phonological prior embedded in the pre-trained WavLM model to mitigate hallucinations. First, we adapt WavLM into a denoising expert via representation distillation to clean its final-layer features. Guided by the model's intrinsic phonological prior, this process enables robust denoising while minimizing linguistic hallucinations. To further reduce acoustic hallucinations, we train the vocoder with a dual-stream representation: the high-level phonetic representation provides clean linguistic content, while a low-level acoustic representation retains speaker identity and prosody. Experimental results demonstrate that PASE not only surpasses state-of-the-art discriminative models in perceptual quality, but also significantly outperforms prior generative models with substantially lower linguistic and acoustic hallucinations.
Speech-Aware Long Context Pruning and Integration for Contextualized Automatic Speech Recognition
Authors: Yiming Rong; Yixin Zhang; Ziyi Wang; Deyang Jiang; Yunlong Zhao; Haoran Wu; Shiyu Zhou; Bo Xu
Venue: AAAI 2026 (main)

Abstract: Automatic speech recognition (ASR) systems have achieved remarkable performance in common conditions but often struggle to leverage long-context information in contextualized scenarios that require domain-specific knowledge, such as conference presentations. This challenge arises primarily due to constrained model context windows and the sparsity of relevant information within extensive contextual noise. To solve this, we propose the SAP^2 method, a novel framework that dynamically prunes and integrates relevant contextual keywords in two stages. Specifically, each stage leverages our proposed Speech-Driven Attention-based Pooling mechanism, enabling efficient compression of context embeddings while preserving speech-salient information. Experimental results demonstrate state-of-the-art performance of SAP^2 on the SlideSpeech and LibriSpeech datasets, achieving word error rates (WER) of 7.71% and 1.12%, respectively. On SlideSpeech, our method notably reduces biased keyword error rates (B-WER) by 41.1% compared to non-contextual baselines. SAP^2 also exhibits robust scalability, consistently maintaining performance under extensive contextual input conditions on both datasets.
Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks
Authors: Dimitrios Rontogiannis; Maxime Peyrard; Nicolas Baldwin; Martin Josifoski; Robert West; Dimitrios Gunopulos
Venue: AAAI 2026 (main)

Abstract: Standard single-turn, static benchmarks fall short in evaluating the nuanced capabilities of Large Language Models (LLMs) on complex tasks such as software engineering. In this work, we propose a novel interactive evaluation framework that assesses LLMs on multi-requirement programming tasks through structured, feedback-driven dialogue.
Each task is modeled as a requirement dependency graph, and an "interviewer" LLM, aware of the ground-truth solution, provides minimal, targeted hints to an "interviewee" model to help correct errors and fulfill target constraints. This dynamic protocol enables fine-grained diagnostic insights into model behavior, uncovering strengths and systematic weaknesses that static benchmarks fail to measure.
We build on DevAI, a benchmark of 55 curated programming tasks, by adding ground-truth solutions and evaluating the relevance and utility of interviewer hints through expert annotation.
Our results highlight the importance of dynamic evaluation in advancing the development of collaborative code-generating agents.
RMO: Towards Better LLM Alignment via Reshaping Reward Margin Distributions
Authors: Yanchi Ru; Yue Huang; Xiangliang Zhang
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) have achieved remarkable success in instruction-following and dialogue tasks, yet aligning them with human preferences remains a critical challenge. Recent advances such as Direct Preference Optimization (DPO) simplify the alignment pipeline by bypassing explicit reward modeling, but they often suffer from suboptimal reward margin distributions, leading to weak supervision signals and reduced discriminative capacity. In this work, we propose Reward Margin Optimization (RMO), a framework that reshapes reward margin distributions during training to improve alignment performance. RMO comprises three components: (1) a Dual Denoising Filtering strategy that filters ambiguous and noisy preference pairs based on reward margin dynamics; (2) Batch Margin Diversification, which maximizes intra-batch margin variance to enhance learning signal diversity; and (3) Pairwise Margin Amplification, an auxiliary regularization term that encourages larger margins between preferred and dispreferred responses. Extensive experiments on multiple LLMs and datasets demonstrate that RMO consistently improves win rates over strong baselines such as DPO and SimPO, while remaining compatible with various preference-based optimization methods. Our results highlight the critical role of reward margin distribution in preference alignment and establish RMO as an effective and scalable enhancement to existing alignment techniques.
Incorporating Token Importance in Multi-Vector Retrieval
Authors: Archish S; Ankit Garg; Kirankumar Shiragur; Neeraj Kayal
Venue: AAAI 2026 (main)

Abstract: ColBERT introduced a late interaction mechanism that independently encodes queries and documents using BERT, and computes similarity via fine-grained interactions over token-level vector representations. This design enables expressive matching while allowing efficient computation of scores, as the multi-vector document representations could be pre-computed offline. 
ColBERT models distance using a Chamfer-style function: for each query token, it selects the closest document token and sums these distances across all query tokens.

In our work, we explore enhancements to the Chamfer distance function by computing a weighted sum over query token contributions, where weights reflect the token importance. Empirically, we show that this simple extension, requiring only token-weight training while keeping the multi-vector representations fixed, further enhances the expressiveness of late interaction multi-vector mechanism. In particular, on the BEIR benchmark, our method achieves an average improvement of 1.28% in Recall@10 in the zero-shot setting using IDF-based weights, and 3.66% through few-shot fine-tuning.
Assessing the Capabilities of LLMs in Humor: A Multi-dimensional Analysis of Oogiri Generation and Evaluation
Authors: Ritsu Sakabe; Hwichan Kim; Tosho Hirasawa; Mamoru Komachi
Venue: AAAI 2026 (main)

Abstract: Computational humor is a frontier for creating advanced and engaging natural language processing (NLP) applications, such as sophisticated dialogue systems. 
While previous studies have benchmarked the humor capabilities of Large Language Models (LLMs), they have often relied on single-dimensional evaluations, such as judging whether something is simply ``funny.'' 
This paper argues that a multifaceted understanding of humor is necessary and addresses this gap by systematically evaluating LLMs through the lens of Oogiri, a form of Japanese improvisational comedy games.
To achieve this, we expanded upon existing Oogiri datasets with data from new sources and then augmented the collection with Oogiri responses generated by LLMs. 
We then manually annotated this expanded collection with 5-point absolute ratings across six dimensions: Novelty, Clarity, Relevance, Intelligence, Empathy, and Overall Funniness. 
Using this dataset, we assessed the capabilities of state-of-the-art LLMs on two core tasks: their ability to generate creative Oogiri responses and their ability to evaluate the funniness of responses using a six-dimensional evaluation. 
Our results show that while LLMs can generate responses at a level between low- and mid-tier human performance, they exhibit a notable lack of Empathy. 
This deficit in Empathy helps explain their failure to replicate human humor assessment. 
Correlation analyses of human and model evaluation data further reveal a fundamental divergence in evaluation criteria: LLMs prioritize Novelty, whereas humans prioritize Empathy. 
We release our annotated corpus to the community to pave the way for the development of more emotionally intelligent and sophisticated conversational agents.
Positional Cognitive Specialization: Where Do LLMs Learn to Comprehend and Speak Your Language?
Authors: Luis Frentzen Salim; Lun-Wei Ku; Hsing-Kuo Kenneth Pao
Venue: AAAI 2026 (main)

Abstract: Adapting large language models (LLMs) to new languages is an expensive and opaque process. Understanding how language models acquire new languages and multilingual abilities is key to achieve efficient adaptation. Prior work on multilingual interpretability research focuses primarily on how trained models process multilingual instructions, leaving unexplored the mechanisms through which they acquire new languages during training. We investigate these training dynamics on decoder-only transformers through the lens of two functional cognitive specializations: language perception (input comprehension) and production (output generation). Through experiments on low-resource languages, we demonstrate how perceptual and productive specialization emerges in different regions of a language model by running layer ablation sweeps from the model’s input and output directions. Based on the observed specialization patterns, we propose CogSym, a layer-wise heuristic that enables effective adaptation by exclusively finetuning a few early and late layers. We show that tuning only the 25% outermost layers achieves downstream task performance within 2–3% deviation from the full finetuning baseline. Unlike similar layer-selection methods, the proposed method requires no extra data or computation while retaining comparable performance, which is especially beneficial for low-resource languages. CogSym yields consistent performance with adapter methods such as LoRA, showcasing generalization beyond full finetuning. These findings provide insights to better understand how LLMs learn new languages and push toward accessible and inclusive language modeling.
Bonsai: Interpretable Tree-Adaptive Grounded Reasoning
Authors: Kate Sanders; Benjamin Van Durme
Venue: AAAI 2026 (main)

Abstract: To develop general-purpose collaborative agents, humans need reliable AI systems that can (1) adapt to new domains and (2) transparently reason with uncertainty to allow for verification and correction. Black-box models demonstrate powerful data processing abilities but do not satisfy these criteria due to their opaqueness, domain specificity, and lack of uncertainty awareness. We introduce Bonsai, a compositional and probabilistic reasoning system that generates adaptable inference trees by retrieving relevant grounding evidence and using it to compute likelihoods of sub-claims derived from broader natural language inferences. Bonsai's reasoning power is tunable at test-time via evidence scaling and it demonstrates reliable handling of varied domains including transcripts, photographs, videos, audio, and databases. Question-answering and human alignment experiments demonstrate that Bonsai matches the performance of domain-specific black-box methods while generating interpretable, grounded, and uncertainty-aware reasoning traces.
AntiDote: Bi-level Adversarial Training for Tamper-Resistant LLMs
Authors: Debdeep Sanyal; Manodeep Ray; Murari Mandal
Venue: AAAI 2026 (main)

Abstract: The release of open-weight large language models (LLMs) creates a tension between advancing accessible research and preventing misuse, such as malicious fine-tuning to elicit harmful content. Current safety measures struggle to preserve the general capabilities of the LLM while resisting a determined adversary with full access to the model's weights and architecture, who can use full-parameter fine-tuning to erase existing safeguards. To address this, we introduce AntiDote, a bi-level optimization procedure for training LLMs to be resistant to such tampering. AntiDote involves an auxiliary adversary hypernetwork that learns to generate malicious Low-Rank Adaptation (LoRA) weights conditioned on the defender model's internal activations. The defender LLM is then trained with an objective to nullify the effect of these adversarial weight additions, forcing it to maintain its safety alignment. We validate this approach against a diverse suite of 52 red-teaming attacks, including jailbreak prompting, latent space manipulation, and direct weight-space attacks. AntiDote is upto 27.4% more robust against adversarial attacks compared to both tamper-resistance and unlearning baselines. Crucially, this robustness is achieved with a minimal trade-off in utility, incurring a performance degradation of upto less than 0.5% across capability benchmarks including MMLU, HellaSwag, and GSM8K. Our work offers a practical and compute efficient methodology for building open-weight models where safety is a more integral and resilient property.