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1 Introduction

https://arxiv.org/html/2407.02518v2

com Abstract Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements

Information Retrieval

https://arxiv.org/list/cs.IR/recent

IR) Tue, 4 Aug 2026 (showing first 34 of 45 entries ) [17] arXiv:2608

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

https://arxiv.org/abs/2104.08663

Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs

Report GitHub Issue

https://arxiv.org/html/2608.03372v1

org GitHub sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one

1 Introduction

Information Retrieval

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

Report GitHub Issue

When LLMs Play the Telephone Game: Cultural Attractors as Conceptual

Solving Zebra Puzzles Using Constraint-Guided Multi-Agent Systems

1 Introduction