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Valentin Malykh

SIGIR 2026×3ACL 2026评测与基准多模态代码对齐与偏好可解释性
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0 一作 · 4 篇 · SIGIR 2026 / ACL 2026

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2026 顶会论文

4
作者位次 #5/8ACL 2026Workshop· Natural Language Processing

Fine-Grained Semantic Comparison of Legal Documents using LLM s

Elisei Rykov · Nikolay Ivanov · Maria Bandulevich · Kseniia Petrushina · Valentin Malykh · Vasily Konovalov · Alexander Panchenko · Ilseyar Alimova

摘要

Frequent revisions of complex regulatory documents in large organizations often introduce inconsistencies and contradictions that are difficult for lawyers and auditors to detect manually. Existing tools rely on character-level diffs and therefore miss paraphrases and semantic shifts. We introduc...

多模态评测与基准对齐与偏好OpenReview 项目页
作者位次 #3/3SIGIR 2026Oral· Information Retrieval (Full)

Hierarchical Embedding Fusion for Retrieval-Augmented Code Generation

Nikita Sorokin · Ivan Sedykh · Valentin Malykh

摘要

Retrieval-augmented code generation commonly conditions a decoder on large retrieved snippets, which couples online cost to repository size and introduces long-context noise. We present Hierarchical Embedding Fusion (HEF), a two-stage repository representation for code completion: (i) an offline...

可解释性效率与系统代码OpenReview
作者位次 #5/8SIGIR 2026Poster· Information Retrieval (Demo)

SLeDoC: System for Legal Document Comparison

Elisei Rykov · Nikolay Ivanov · Kseniia Petrushina · Maria Bandulevich · Valentin Malykh · Vasily Konovalov · Alexander Panchenko · Ilseyar Alimova

摘要

Frequent revisions of complex, lengthy regulatory documents in large organizations often create inconsistencies and contradictions that lawyers and auditors find difficult to detect manually. Existing tools rely on character-level diffs that fail to capture paraphrases and semantic shifts. We pre...

多模态评测与基准检索与知识OpenReview
作者位次 #4/4SIGIR 2026Poster· Information Retrieval (Short)

SAGE: Solver-Aligned Guided Exploration

Nikita Sorokin · Ivan Sedykh · Timur Ionov · Valentin Malykh

摘要

Modern code agents achieve strong results on challenging software engineering benchmarks such as SWE-bench, but solving each issue remains expensive: most inference cost is spent not on patch generation, but on repository exploration and search, accounting for up to 56% of tokens in our experimen...

代码强化学习与后训练评测与基准OpenReview

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