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版本更新 · v3arXiv:2606.04120

SaliMory: Orchestrating Cognitive Memory for Conversational Agents

Kai Zhang, Xinyuan Zhang, Hongda Jiang, Shiun-Zu Kuo, Hyokun Yun, Ejaz Ahmed, Shereen Oraby, Ziyun Li 等 14 位作者

原文摘要

Recall and personalization, enabled by memory, are pivotal to a lifelong conversational agent. However, simply expanding context windows with raw retrieval degrades reasoning quality, while training memory agents via standard reinforcement learning creates a severe credit assignment bottleneck in a multi stage pipeline. To solve this, we introduce SaliMory, a framework that learns to manage and utilize a cognitively structured memory spanning user facts, preferences, and working memory. By introducing novel stage wise process rewards and reward decomposed contrastive refinement, SaliMory provides isolated supervision for distinct memory operations (selective filtering, consolidation, and cue driven recall) end to end. Across four datasets, SALIMORY records the highest Good Personalization rate at 39.8% and the best QA accuracy at 72.9%, while consistently producing the least vague and most factual memories, keeping vagueness under 20% and factuality above 89%.

cs.CL · cs.AI

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1059ACL Anthology · ACL 2026

BiMol-Diff: A Unified Diffusion Framework for Molecular Generation and Captioning

Aditya Hemant Shahane, Anuj Kumar Sirohi, Devansh Arora, Nitin Kumar, Prathosh AP Indian Institute of Science, Indian institute of science, Bangalore, Sandeep Kumar Indian Institute of Technology Delhi

原文摘要

Bridging molecular structures and natural language is essential for controllable design. Autoregressive models struggle with long-range dependencies, while standard diffusion processes apply uniform corruption across positions, which can distort structurally informative tokens. We present BiMol-Diff, a unified diffusion framework for the paired tasks of text-conditioned molecule generation and molecule captioning. Our key component is a token-aware noise schedule that assigns position-dependent corruption based on token recovery difficulty, preserving harder-to-recover substructures during the forward process. On ChEBI-20 and M3-20M, BiMol-Diff improves molecule reconstruction with a 15.4% relative gain in Exact Match and achieves strong captioning results, attaining best BLEU and BERTScore among compared baselines. These results indicate token-aware noising improves fidelity in molecular structure-language modeling

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-short.58ACL Anthology · ACL 2026

Revisiting Evaluation of Question Answering Systems in Low-Resource Indic Languages: Bridging Human and Metric Alignment

Anuj Kumar, Satyadev Ahlawat Indian Institute of Technology Jammu, Yamuna Prasad Indian Institute of Technology Jammu, Virendra Singh Indian Institute of Technology Bombay, Indian Institute of Technology, Bombay

原文摘要

Evaluating Question Answering (QA) systems in low-resource Indic languages remains challenging due to the scarcity of annotated data, high linguistic diversity, and the absence of reliable evaluation metrics. Many Indian languages are severely underrepresented, making it difficult to accurately assess the performance of Large Language Models (LLMs) on QA tasks. Commonly used metrics like BLEU, ROUGE-L, and BERTScore, while successful in machine translation and resource-rich scenarios, tend to perform poorly in low-resource QA settings. These metrics often exhibit issues such as compressed scoring ranges, excessive zero scores, and weak alignment with human judgments. To overcome these limitations, this work introduces the LRM^{2}QAS (Language Robust Multi-aspect Metrics for Question Answering Systems). This composite evaluation framework integrates semantic similarity, factual completeness, numerical accuracy, and contextual relevance. The proposed metric is evaluated across eight Indic-language QA tasks using multiple LLMs, as well as on open-domain benchmarks NaturalQuestions (NQ) and TriviaQA (TQ). Across all settings, LRM^2QAS demonstrates stronger agreement with human evaluation, as measured by Pearson, Spearman, and Kendall correlation coefficients. Experimental findings highlight that LRM^2QAS provides more precise distinctions between model outputs and aligns more closely with human judgment, offering a reliable framework for evaluating multilingual QA in low-resource Indic languages.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表