Good Papers

Showing papers from Research, Microsoft Show all papers

45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026MicrosoftMicrosoft ResearchResearch, MicrosoftMicrosoft Research, Redmond, WAMeta, transfer & few-shot learning

Test-Time Learning with an Evolving Library

Weijia Xu, Alessandro Sordoni, Chandan Singh, Zelalem Gero and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Independent ResearcherResearch, MicrosoftMicrosoftIn-context learning & prompting

CAPO: A Primal-Dual Framework for Constraint-Aware Prompt Optimization

Victor Ye Dong, Reid Pryzant, Yi Liu, Jian Jiao

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026CUHK & Microsoft Research AsResearch, MicrosoftMicrosoftU Illinois at Urbana-ChampaignThe Chinese University of Hong KAgent benchmarks & environments

RoutingBench: Can Agentic Routing Analysis Scale to Production Datacenter Networks?

Wenlong Ding, Zhixiong Niu, Jianan Yang, Fajun Zhang and 5 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026SoochowResearch, MicrosoftMicrosoft Research AsiaStony BrookPekingRL for LLMs

Agent$^2$ RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?

Wanyi Chen, Xiao Yang, Xu Yang, Tianming Sha and 6 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026MicrosoftU WashingtonStanfordResearch, MicrosoftPUC-RIORL for LLMs

Multi-Turn RL Makes Small Language Model Competitive for Optimization Modeling

Xinzhi Zhang, Zeyi Chen, Humishka Zope, Hugo Barbalho and 5 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026SpotlightTsinghuaMicrosoftU TorontoMassachusetts Institute of TechnResearch, MicrosoftMulti-agent LLM systems

STAR-Math: Multi-Agent Mathematical Reasoning under Persistent Meta-Strategic Supervision

Jiaao Wu, Xian Zhang, Hanzhang Liu, Sophia Zhang and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026PekingResearch, MicrosoftAgent memory

MemContract: Contract-Sensitive Evaluation for Mutable Agent Memory

Charley Li, Alice Cao

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
86%Must read
?Must readVote to see the score
NeurIPS 2026Simon FraserComputing Science, Simon FraserTrinityMicrosoft ResearchResearch, MicrosoftLLM evaluation & benchmarks

RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities

RustMizan provides compilable Rust vulnerability benchmarks with mutation-based contamination tests, finding frontier LLM agents achieve 56-65% binary detection but ~20% line-localization F1 that adversarial cues reduce by 27%.

Tarek Elsayed, Shiping Yang, Eunsong Koh, Sanika Goyal and 12 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 2/5