AI Research Highlights

Vasilis Kontonis, Yuchen Zeng, Shivam Garg et al.

MEMENTO trains reasoning models to summarize their own working state into reusable memory blocks, cutting KV-cache costs about 2.5x and boosting throughput without giving up math, science, or coding accuracy.

Dhruv Atreja, Julia White, Nikhil Nayak et al.

Pioneer Agent turns small-model adaptation into an automated closed loop that diagnoses failures, curates new data, retrains under regression constraints, and materially improves production-style tasks.

Kaiyang Qian, Xinmin Fang, Zhengxiong Li

MPAC proposes a real coordination protocol for multi-owner agent systems, adding structured conflict handling and governance so agents can safely share state instead of silently clobbering each other.

Nastaran Darabi, Amit Ranjan Trivedi

ProGAL-VLA adds verified grounding and prospective sub-goals to VLA robots, sharply improving instruction sensitivity, ambiguity handling, and robustness under perturbation.

Tiantian He, Yihang Chen, Keyue Jiang et al.

EE-MCP shows how MCP-plus-GUI agents can self-improve by generating environments, synthesizing gap tasks, and accumulating reusable experience, with clear gains across desktop apps.

Kyle Whitecross, Negin Rahimi

RecaLLM tackles the lost-in-thought problem by interleaving reasoning with explicit in-context retrieval, giving long-context models a practical way to stay grounded at up to 128K tokens.

Siyuan Xu, Shiyang Li, Xin Liu et al.

COVERT turns synthetic tool-use data into reward-checkable RL environments, making it much easier to harden agent tool calling against ambiguity, distractor tools, and noisy outputs.

Chenhao Ye, Huaizheng Zhang, Mingcong Han et al.

TensorHub attacks a painful RL-systems bottleneck by serving model weights from replicas already resident on GPUs, dramatically reducing rollout stalls in elastic and cross-datacenter training.

Hadas Orgad, Boyi Wei, Kaden Zheng et al.

This mechanistic safety paper argues harmful generation is concentrated in a compact, reusable weight subspace, offering a concrete explanation for why narrow fine-tuning can trigger broad misalignment.

Yucheng Shen, Jiulong Wu, Jizhou Huang et al.

VISOR pushes visual RAG toward real agent behavior with iterative search, evidence-space tracking, and drift control for long-horizon multimodal question answering over documents.

Yushi Feng, Junye Du, Qifan Wang et al.

CORA adds conformal risk control to mobile GUI agents so teams can set explicit harm budgets and abstain before risky clicks instead of trusting heuristic guardrails.

Mohamed Elfeki, Tu Trinh, Kelvin Luu et al.

HiL-Bench measures whether agents know when to ask for missing information, exposing a major reliability gap that standard pass/fail coding benchmarks mostly hide.

Peng Ding

LLM-Rosetta introduces a neutral intermediate representation for major LLM APIs, giving builders a credible path away from brittle one-off provider adapters and vendor lock-in.

Jingyu Zhang, Tianjian Li, William Jurayj et al.

Many-Tier Instruction Hierarchy shows today's agents break down when instruction privilege gets more granular, making it a useful stress test for serious multi-tool and multi-role deployments.

Suhana Bedi, Ryan Welch, Ethan Steinberg et al.

HealthAdminBench gives computer-use agents a rare end-to-end GUI benchmark in a real workflow domain and shows that strong subtask scores still collapse into poor task completion.