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Total papers: 6 Search mode: keyword Shortlist (0) RSS

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SEAL: Can Saturated Benchmarks Be Revived by LLM-as-a-Meta-Judge?

Jiamin Chen, Yidi Wu, Qiexiang Wang, Qianben Chen, Yuchen Li, Yansen Zhang · May 28, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% Moderate protocol signal Freshness: Warm Status: Ready
Pairwise Preference Automatic Metrics MathCoding
  • Therefore, we present Seeded Elimination with Adaptive LLM-as-a-Meta-Judge, a self-improving evaluation protocol for extracting latent ranking signal from saturated benchmarks.
  • We evaluate SEAL on multiple saturated benchmarks covering code generation, mathematical reasoning, knowledge-intensive question answering, and tool-use agent task completion.
Open paper
Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles

Jinyang Wu, Guocheng Zhai, Ruihan Jin, Yuhao Shen, Zhengxi Lu, Fan Zhang · May 21, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% Moderate protocol signal Freshness: Warm Status: Ready
Expert Verification Automatic Metrics MathCoding
  • In this paper, we present Maestro (Multimodal Agent for Expert-Skill Targeted Reinforced Orchestration), a Reinforcement Learning (RL)-driven orchestration framework that reframes heterogeneous multimodal tasks as a sequential…
  • We evaluate Maestro across ten representative multimodal benchmarks spanning mathematical reasoning, chart understanding, high-resolution perception, and domain-specific analysis.
Open paper
Proof-of-Guardrail in AI Agents and What (Not) to Trust from It

Xisen Jin, Michael Duan, Qin Lin, Aaron Chan, Zhenglun Chen, Junyi Du · Mar 6, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 98% Moderate protocol signal Freshness: Cold Status: Ready
Red Team Automatic Metrics MathLaw
  • As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces a threat where safety measures are falsely advertised.
  • To generate proof, the developer runs the agent and guardrail inside a Trusted Execution Environment (TEE), which produces a TEE-signed attestation of guardrail code execution verifiable by any user offline.
Open paper
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

Ailin Huang, Ang Li, Aobo Kong, Bin Wang, Binxing Jiao, Bo Dong · Feb 11, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 98% High protocol signal Freshness: Cold Status: Ready
Pairwise Preference Tool Use MathCoding
  • We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency.
  • Step 3.5 Flash demonstrates strong performance across agent, coding, and math tasks, achieving 85.4% on IMO-AnswerBench, 86.4% on LiveCodeBench-v6 (2024.08-2025.05), 88.2% on tau2-Bench, 69.0% on BrowseComp (with context management), and…
Open paper
TRIMS: Trajectory-Ranked Instruction Masked Supervision for Diffusion Language Models

Lingjie Chen, Ruizhong Qiu, Yuyu Fan, Yanjun Zhao, Hanghang Tong · Apr 1, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% Moderate protocol signal Freshness: Warm Status: Fallback
Automatic Metrics Long Horizon MathCoding
  • Experiments on LLaDA and Dream across math and coding benchmarks show that TRIMS significantly improves the accuracy-parallelism trade-off over both standard MDLM training and train-free acceleration baselines, while achieving competitive…
Open paper
Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

Roy Miles, Aysim Toker, Andreea-Maria Oncescu, Songcen Xu, Jiankang Deng, Ismail Elezi · Feb 26, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 98% Moderate protocol signal Freshness: Cold Status: Fallback
Automatic Metrics Long Horizon MathCoding
  • This modular pipeline separates exploration (diffusion) from evaluation and solution synthesis, avoiding monolithic unified hybrids while preserving broad search.
  • Across math reasoning benchmarks, we find that step-level recombination is most beneficial on harder problems, and ablations highlight the importance of the final AR solver in converting stitched but imperfect rationales into accurate…
Open paper

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