Proof pending. Core topic summary fields are still materializing.
Large language model (LLM) agents are evolving to enhance their capabilities in various domains, including bug detection, economic modeling, and workflow optimization. Recent advancements, such as AnyPoC, automate the generation of proof-of-concept tests for bug validation, significantly improving the reliability of automated bug detection. Market-Bench evaluates LLMs in economic tasks, revealing performance disparities among agents in competitive environments. Additionally, frameworks like SAGE and CLEAR focus on optimizing modeling strategies and context generation, respectively, enhancing LLM performance in complex tasks. These developments are crucial for builders as they enable more efficient and effective use of LLMs, facilitating automation and improving productivity across diverse applications in software development and research.
Topic-specific paper and score movement from the daily diff ledger.
While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation, limiting the practicality of automated bug detecti...
Large language model (LLM) agents are increasingly deployed to automate productivity tasks (e.g., email, scheduling, document management), but evaluating them on live services is risky due to potentia...
Large Language Models (LLMs) increasingly prioritize user validation over epistemic accuracy-a phenomenon known as sycophancy. We present The Silicon Mirror, an orchestration framework that dynamicall...
The ability of large language models (LLMs) to manage and acquire economic resources remains unclear. In this paper, we introduce \textbf{Market-Bench}, a comprehensive benchmark that evaluates the ca...
Modern coding agents increasingly delegate specialized subtasks to subagents, which are smaller, focused agentic loops that handle narrow responsibilities like search, debugging or terminal execution....
Large language model agents rely on effective model context to obtain task-relevant information for decision-making. Many existing context engineering approaches primarily rely on the context generate...
One-shot Program-of-Thought (PoT) emits a Python program that prints a primitive-action plan; a single invalid action silently invalidates the trajectory. We introduce RePoT (Recoverable PoT): a deter...
Effectively configuring scalable large language model (LLM) experiments, spanning architecture design, hyperparameter tuning, and beyond, is crucial for advancing LLM research, as poor configuration c...
Evaluating the reasoning capabilities of Large Language Models (LLMs) for complex, quantitative financial tasks is a critical and unsolved challenge. Standard benchmarks often fail to isolate an agent...
Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets. Today, even with AI coding assistants like...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID llm-agents | Route /topic/llm-agents
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/llm-agentsMCP example
{
"tool": "search_papers",
"arguments": {
"query": "LLM Agents",
"cluster": "LLM Agents"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "LLM Agents",
"normalized_query": "llm-agents",
"route": "/topic/llm-agents",
"paper_ref": null,
"topic_slug": "llm-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.