The study presents AgentSWE, a benchmark evaluating coding agents' ability to build agents aligned with user requirements. The research found that existing agents fail to accurately fulfill tasks, as there is a significant gap between demonstrated and actual performance.
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This research is important for understanding current limitations in agent creation, which can help improve development methods.
The study explores Adversarial Heuristic Learning (AHL), where AI agents refine game strategies with fixed model weights. The AAArena benchmark includes 12 games and 1,920 archived programs, evaluated through protocols mimicking real competitions. Results show agents can improve strategies but face challenges in complex games.
5 days agoDaily PapersThe paper introduces S-Space, a linear subspace in multimodal language models where object-token representations encode continuous spatial coordinates. It demonstrates that S-Space serves as an internal workspace for spatial reasoning, enabling accurate spatial judgments through perception, internal reasoning, and selective verbal access, while highlighting limitations in spatial transformations across viewpoints.
4 days agoDaily PapersThe paper introduces TerminalHorizon, an automated data synthesis engine for long-horizon terminal agents. It constructs 1.5K task environments and fine-tunes Qwen3.5-27B to create TerminalHorizon-27B, achieving performance gains across multiple benchmarks. The model demonstrates improved ability to experiment, refine methods, and retain effective improvements in final results.
4 days agoDaily PapersVIS-Ground is a method that transforms heterogeneous input context into an executable constraint model for video generation using structured context abstraction, generation constraints induction, and constrained video generation. The approach demonstrates improvements over existing systems.
6 days agoDaily PapersTetris3D is a generative framework for 3D scene reconstruction from a single image, ensuring objects are geometrically and physically coherent. It uses explicit conditioning on surrounding objects' geometry and physical relationships, along with a physics-based dataset called ComOb for training and evaluation.
6 days agoDaily PapersMulti-Stream Attention Residuals (MSAR) extends the AttnRes method by generalizing the attention mechanism to multi-head depth, achieving significant performance improvements with minimal additional overhead. MSAR leverages multi-scale causal convolutions to draw stream diversity from sequence context, allowing different heads to retrieve historical representations through distinct contextual views.
4 days ago