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arXivColumbiaAmazonMulti-agent LLM systems

Receiver-Conditioned Latent Communication gives 94% CacheBack

CacheBack uses receiver-conditioned filtering of sender KV caches via attention weights to cut transferred state by 75%, boosting multi-agent accuracy by 14.7 points and reducing latency 3.2x versus text.

Maximillian Rossi, Prajwal Raghunath, Haoqing Xuan, Yusen Zhang and 1 more

Published Sep 25, 2026 · 0 citations · ▲ 11 on Hugging Face · Code ★ 6

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 0/5
69%Highly rated
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ACL 2026U Notre DameAmazonUniversité de MontréalInstruction tuning

Instant Personalized Large Language Model Adaptation via Hypernetwork

A hypernetwork enables instant personalized large language model adaptation by generating user-specific parameters directly from user data.

Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li and 7 more

Published 2026 · 1 citation

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3/20 AI panelreviewers recommend it

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AI panel: 3 of 20 reviewers recommend it
lenient 2/5
medium 1/10
strict 0/5
67%Highly rated
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NAACL 2025U Notre DameAmazonWorcester Polytechnic InstituteLLM evaluation & benchmarks

IHEval: Evaluating Language Models on Following the Instruction Hierarchy

IHEval evaluates language models on instruction hierarchy compliance, finding they frequently disregard system-level priority instructions.

Zhihan Zhang, Shiyang Li, Zixuan Zhang, Xin Liu and 10 more

Published 2025 · 4 citations

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2/20 AI panelreviewers recommend it

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AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
74%Highly rated
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ACM Transactions on Knowledge Discovery from Data 2024AmazonTexas A&MRiceLLM evaluation & benchmarks

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

This survey guides practitioners in deploying LLMs across NLP tasks, covering model selection, data effects, use cases, biases, efficiency, and practical limitations.

Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han and 5 more

Published Feb 28, 2024 · 503 citations

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9/21 AI panelreviewers recommend it

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AI panel: 9 of 21 reviewers recommend it
lenient 5/5
medium 4/11
strict 0/5