SimWorld Studio deploys an evolving coding agent to create adaptive 3D environments that co-evolve with embodied learners, delivering 18-point success-rate gains over fixed environments in navigation benchmarks.
super hub Canonical reference
Toolformer: Language Models Can Teach Themselves to Use Tools
Canonical reference. 87% of citing Pith papers cite this work as background.
196
Pith papers citing it
Background
87% of classified citations
abstract
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller models excel. In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q\&A system, two different search engines, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.
hub tools
citation-role summary
background 44
baseline 1
dataset 1
method 1
citation-polarity summary
claims ledger
- abstract Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller models excel. In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. Thi
authors
co-cited works
representative citing papers
ShadowMerge exploits relation-channel conflicts to poison graph-based agent memory, achieving 93.8% average attack success rate on Mem0 and real-world datasets while bypassing existing defenses.
The khipu problem frames a governance failure in distributed AI where interpretive continuity is lost even when traces remain, requiring infrastructure to preserve reading practices rather than only data retention.
A language-model-driven agentic AI system autonomously executes multi-stage physics experiments at a production synchrotron light source, reducing preparation time by two orders of magnitude while upholding safety constraints.
RepairAgent autonomously repairs 164 bugs on Defects4J including 39 not fixed by prior techniques by treating an LLM as an agent that invokes tools via a finite state machine and dynamic prompts.
DSPy compiles short declarative programs into LM pipelines that self-optimize and outperform both standard few-shot prompting and expert-written chains on math, retrieval, and QA tasks.
Mind2Web is the first large-scale dataset of real-world web tasks for developing generalist language-guided agents that complete complex actions on diverse websites.
API-Bank is a new benchmark and training dataset for tool-augmented LLMs that shows fine-tuned models can approach GPT-3.5 tool-use effectiveness.
TO-Master is an LLM agent framework that orchestrates finite-element topology optimization from conversational inputs, supporting 2D/3D compliance, thermal, stress-constrained, and multi-load cases while reproducing benchmarks without user code.
TRIAGE augments GRPO with role-typed segment rewards derived from a judge that detects regression and exploration, yielding higher success rates and fewer turns on ALFWorld, Search-QA, and WebShop.
The paper defines entity binding failures as a distinct error category in tool-augmented agents separate from tool selection errors and evaluates entity-aware mechanisms that eliminate such failures in a controlled diagnostic setting.
Introduces brain-prompt injection attacks on BCI-to-LLM agent pipelines and defines a Route-Safety Audit Contract with separation theorem, C3 decomposition, and split-conformal calibration to bound false-accept rates on EEG data.
Lightfall provides an API-first, LLM-addressable control platform for synchrotron beamlines enabling natural language extensions by scientists through plugin skills.
AIP models skills as graphs of discrete steps connected by typed I/O edges under a validated schema, raising agent mean reward from 0.60 to 0.71 and pass rate from 53% to 67% on 27 SkillsBench tasks while enabling node-level fixes.
Sandboxed coding agents with text+image access match or outperform native omnimodal models on audio-video benchmarks by converting tasks into code-driven retrieval and processing.
Tool schema compression by 44-50% enables agentic RAG at 8K context where uncompressed schemas fail, with +20.5 pp exact match lift across models and scaling to over 800 tools.
Hylos proposes operability contracts and SpatialTransactions to maintain scene-scale state and validate changes in model-generated 3D, shifting evaluation from visual quality to practical operability.
MemGym unifies agent gyms into a memory benchmark with isolated scoring across tool-use, research, coding, and computer-use regimes plus a lightweight reward model for tractable coding evaluation.
LQM-ContextRoute routes LLM tool calls via latency-quality matching in a contextual bandit, improving F1 by 2.18 pp, accuracy by up to 18 pp, and NDCG by 2.91-3.22 pp over SW-UCB on web-search, StrategyQA, and retriever benchmarks.
Mobius Injection exploits semantic closure in LLM agents to enable single-message AbO-DDoS attacks achieving up to 51x call amplification and 229x latency inflation.
A new evaluation protocol shows agent memory reliability degrades variably with added irrelevant sessions depending on agent, memory interface, and scale.
MemFlow routes queries by intent to tiered memory operations, nearly doubling accuracy of a 1.7B SLM on long-horizon benchmarks compared to full-context baselines.
LLM adaptive exploration via runtime code execution outperforms static query generation for information extraction from heterogeneous BIM models on the new ifc-bench v2 benchmark.
Developers use LLMs like ChatGPT mainly for knowledge acquisition and code generation at the detailed design level, reporting benefits such as better technology selection and early flaw detection alongside limitations like lengthy outputs, incorrect code, and hallucinations.
citing papers explorer
-
Beyond the Black Box: Interpretability of Agentic AI Tool Use
Introduces a mechanistic interpretability toolkit with SAEs and linear probes to monitor tool-use decisions and risk in AI agents from internal activations before execution.
-
An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration
Experience-RAG Skill is a reusable agent skill that selects retrieval strategies via experience memory, achieving 0.8924 nDCG@10 on BeIR/nq, hotpotqa, and scifact while outperforming fixed retriever baselines.
-
Vibe Medicine: Redefining Biomedical Research Through Human-AI Co-Work
Vibe Medicine proposes directing AI agents via natural language for end-to-end biomedical workflows using LLMs, agent frameworks, and a curated collection of over 1,000 medical skills.
-
On Accelerating Grounded Code Development for Research
A framework is introduced that connects coding agents to user-uploaded research repositories and enforces domain rules to enable context-aware code development in specialized scientific fields.
-
SciFi: A Safe, Lightweight, User-Friendly, and Fully Autonomous Agentic AI Workflow for Scientific Applications
SciFi is a safe, lightweight agentic AI framework that automates structured scientific tasks with minimal human intervention via isolated environments and layered self-assessing agents.
-
Aethon: A Reference-Based Replication Primitive for Constant-Time Instantiation of Stateful AI Agents
Aethon enables near-constant-time instantiation of stateful AI agents via reference-based replication over compositional views, layered memory, and copy-on-write semantics.
-
OpenKedge: Governing Agentic Mutation with Execution-Bound Safety and Evidence Chains
OpenKedge redefines AI agent state mutations as a governed process using intent proposals, policy-evaluated execution contracts, and cryptographic evidence chains to enable safe, auditable agentic behavior.
-
ActionNex: A Virtual Outage Manager for Cloud Computing
ActionNex is an agentic system for cloud outage management that compresses multimodal signals into critical events, uses hierarchical memory for reasoning, and recommends actions with 71.4% precision on real Azure outages.
-
The Rise and Potential of Large Language Model Based Agents: A Survey
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
-
A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
- Grounded Iterative Language Planning: How Parameterized World Models Reduce Hallucination Propagation in LLM Agents
- The Token Not Taken: Sampling, State, and the Stochasticity of AI Agents
- Don't Make the LLM Read the Graph: Make the Graph Think
- UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents