Agents are increasingly interacting with other agents that act for different principals (people or organizations on whose behalf they work), such as a buyer's personal agent negotiating with an enterprise's seller agent. In such agent-to-agent (A2A) scenarios, each agent has its own environment and holds information an...
Sarath Shekkizhar, Prathyusha Jwalapuram, Zi-Xuan Ke et al.· 0 citations
This work introduces HASP (Harnessing LLM Agents with Skill Programs), a new framework that upgrades skills into executable Program Functions (PFs) that act as executable guardrails that activate on failure-prone states and modify the next action or inject corrective context.
This work characterize OPD across nine student-teacher pairs spanning 2x to 53x parameter ratios and shows that OPD effectiveness depends on student-teacher compatibility rather than teacher scale alone, and suggests that each post-training stage should be chosen not only for the capability it adds, but for the learnin...
Emre Can Acikgoz, Yang Li, Z. Liu et al.· 0 citations
Just-in-Time Memory (JitMem) consistently outperforms no-memory agents as well as heuristic and learned write-time memory methods, improving over the strongest baseline by 16.2, 16.3, and 3.9 absolute success-rate points, respectively.
Four parallelism plans are left unbounded by the parallelism plans in common use, and each grows differently: expert dispatch with the routing matrix, the vocabulary projection with tokens times vocabulary, gradient checkpoint boundaries with depth times sequence length, and optimizer state with parameter count.
Shrey Pandit, Xuan-Phi Nguyen, Yiran Zhao et al.· 0 citations
EvoHarnessBench is introduced, a benchmark for evaluating agents under controlled harness evolution across three axes (tools, skills, and agents), and establishes harness evolution as a distinct challenge for building agents that can keep pace with an evolving harness while preserving previously effective behavior.
Zi-Xuan Ke, Vaidehi Patil, Hai-Zhou Shi et al.· 5 citations· ⚡1
Experiments spanning mathematical reasoning, multi-domain STEM, code generation, and multi-turn agentic tasks show that RISE outperforms RLVR-only training and on-policy self-distillation across all settings.
Yang Li, Semih Yavuz, Shafiq Joty· 5 citations· ⚡1
This work introduces VisEditBench, a benchmark of 1,395 human-annotated visualization code-editing tasks grounded in realistic visualization workflows and failure cases, and proposes VisEditAgent, a render-grounded editing framework that iteratively generates, executes, validates, and refines candidate edits.
It is found that process-level verification does not consistently improve performance and frequently exhibits high variance, highlighting the difficulty of reliably evaluating partial multi-agent trajectories.
Vishal Venkataramani, Haizhou Shi, Zi-Xuan Ke et al.· arXiv.org· 6 citations
The results suggest that dense credit assignment through distillation can be effective when its likelihood-based scores are empirically validated as meaningful proxies for outcome-relevant credit, when this alignment does not hold.
Xuan-Phi Nguyen, Z. Liu, Yang Li et al.· 3 citations
DSAgentBench is introduced, the first benchmark to evaluate whether agents can automate full data-science workflows inside real computer environments, and reveals a substantial capability gap between current agentic systems and real data-science workflows.
Mizanur Rahman, Mohammed Saidul Islam, Ridwan Mahbub et al.· 0 citations
River, a simple training recipe that improves reward quality by filtering low-quality environments and augmenting outcome rewards with process-level behavior regularization is proposed, which achieves the best performance among evaluated open-source RL-trained 8B models across four terminal-agent benchmarks.
Yi-Fan Yao, Bo Pang, Xuan-Phi Nguyen et al.· 2 citations
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