activity
20242026
most citedContext Engineering 2.0: The Context of Context Engineering

3 citations · 9 across the 16 of their papers we have counts for

collaborators
Showing 2026Show all

8 papers · 1 filter

cs.RO2026

GIVE: Grounding Human Gestures in Vision-Language-Action Models

Pengfei Liu, Gen Li, Junqiao Fan +4

Human communication is inherently multimodal, where language is often accompanied by non-verbal cues such as gestures to convey intentions. However, current Vision-Language-Action…

cs.CL2026

SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs

Jie Sun, Yu Liu, Lu Han +9

While transformer-based Large Language Models (LLMs) theoretically support massive context windows, they suffer from severe performance degradation when processing long numerical s…

cs.CL2026

CODA: Difficulty-Aware Compute Allocation for Adaptive Reasoning

Siye Wu, Jian Xie, Yikai Zhang +1

The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks. However, it often falls into another t…

cs.SE2026

LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces

Yukang Feng, Jianwen Sun, Zelai Yang +16

Recent advances in AI-assisted programming have empowered agents to execute complex workflows via command-line interfaces, however, existing benchmarks are limited by short task ho…

cs.LG2026

daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently

Mohan Jiang, Dayuan Fu, Junhao Shi +8

While Large Language Models (LLMs) excel at short-term tasks, scaling them to long-horizon agentic workflows remains challenging. The core bottleneck lies in the scarcity of traini…

cs.LG2026

IRPM: Intergroup Relative Preference Modeling for Pointwise Generative Reward Models

Haonan Song, Qingchen Xie, Huan Zhu +12

Generative Reward Models (GRMs) have demonstrated strong performance in reward modeling, due to their interpretability and potential for refinement through reinforcement learning (…

cs.SE2026

daVinci-Dev: Agent-native Mid-training for Software Engineering

Ji Zeng, Dayuan Fu, Tiantian Mi +14

Recently, the frontier of Large Language Model (LLM) capabilities has shifted from single-turn code generation to agentic software engineering-a paradigm where models autonomously…

cs.AI2026

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts

Keyu Li, Junhao Shi, Yang Xiao +11

Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain f…