Proof partial. Core topic fields are ready, but questions or supporting reports are still catching up.
Recent developments in the field of AI agents are focused on enhancing adaptability, memory, and reliability, addressing critical challenges in real-world applications. Frameworks like MetaClaw and Memanto are pioneering continuous learning and efficient memory management, enabling agents to evolve in response to user needs without downtime or excessive computational overhead. The introduction of community-driven tools like OpenTools aims to improve the reliability of tool-using agents by standardizing interfaces and facilitating collaborative improvements. Meanwhile, OxyGent is advancing multi-agent systems by promoting modularity and observability, essential for complex industrial environments. Benchmarks such as SEA-Eval and AutomationBench are being established to rigorously assess agents' long-term performance and cross-application orchestration capabilities, respectively. These shifts indicate a concerted effort to transition from static task execution to dynamic, self-evolving agents capable of navigating complex workflows, ultimately enhancing their applicability in enterprise settings and beyond.
Topic-specific paper and score movement from the daily diff ledger.
Large language model (LLM) agents are increasingly used for complex tasks, yet deployed agents often remain static, failing to adapt as user needs evolve. This creates a tension between the need for c...
Computer-Using Agents (CUAs) aim to autonomously operate computer systems to complete real-world tasks. However, existing agentic systems remain difficult to scale and lag behind human performance. A ...
Tool-integrated LLMs can retrieve, compute, and take real-world actions via external tools, but reliability remains a key bottleneck. We argue that failures stem from both tool-use accuracy (how well ...
What if a browser agent could learn your work simply by watching you do it? We present cotomi Act, a browser-based computer-using agent that combines reliable multi-step task execution with persistent...
Computer Use Agents (CUAs) can act through both atomic GUI actions, such as click and type, and high-level tool calls, such as API-based file operations, but this hybrid action space often leaves them...
Enterprise agents are increasingly expected to operate autonomously across tools and interfaces, yet production deployments require governance by construction. Systems must specify which actions are a...
Tool calling extends large language models (LLMs) by enabling grounded interaction with external executable interfaces, thereby supporting environment-coupled problem solving. However, mainstream in-c...
Finite Element Analysis (FEA) serves as the cornerstone of modern engineering design. However, its workflow is inherently complex and relies heavily on domain expertise. Although recent efforts have i...
The emergence of LLMs has catalyzed a paradigm shift in autonomous agent development, enabling systems capable of reasoning, planning, and executing complex multi-step tasks. However, existing agent f...
As AI agents transition from research prototypes to enterprise production systems, the tool interfaces they consume remain rooted in human-oriented CRUD paradigms. This paper identifies five fundament...
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Canonical route: /topics
Agent Handoff
Canonical ID agents | Route /topic/agents
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/agentsMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Agents",
"cluster": "Agents"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Agents",
"normalized_query": "agents",
"route": "/topic/agents",
"paper_ref": null,
"topic_slug": "agents",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.