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LLM-based agentic systems are increasingly combined with CoT reasoning to solve tasks that require accurate reasoning, external knowledge, and multi-step…
Prediction errors from video world models are often treated as physical surprise, but they can also reflect appearance, static content, motion, or ordinary…
Most robot policies, whether trained by imitation learning (IL) or reinforcement learning (RL), map observations to action values at discrete control steps and…
A scientific agent can give the same verdict while reading and citing different evidence.
As coding agents achieve increasingly high success rates on software engineering benchmarks, recent efforts have sought to increase task complexity through…
Scientific names increasingly connect papers from different research topics, leaving readers to recover a paper's identity from context.
Coding agents solve repository issues through long sequences of exploration, editing, and validation.
Computer graphics is built on code. Large language model (LLM) agents can now generate graphics code and iteratively refine it through various feedback.
Humans continuously acquire knowledge from carefully crafted materials such as books and tutorial videos, carrying that understanding into future tasks.
Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality.
AI reviewers are increasingly used to assess scientific manuscripts, yet it remains unclear whether their judgments reflect the improvements authors make…
Software development sessions often involve several unfinished tasks rather than one issue carried through to completion.
Long-horizon terminal tasks are used to evaluate and train agents. Human-curated datasets are scarce, so many synthesis methods have emerged.
Heterogeneous embedded boards pair a Linux application processor with a real-time microcontroller (MCU), which suggests moving a task between the two domains…
Anticipating financial outcomes requires synthesizing evolving evidence and reasoning under uncertainty, making deep research systems promising tools for…
Agent decisions depend on empirical relationships that deployment observations reveal and task descriptions alone may leave unresolved.
Streaming video LLMs can now commentate live gameplay, but they are trained and tested on the same games.
Psychotherapy clients may experience distress before they can coherently describe it, and their expression changes with emotion, perceived safety, and trust.
Recent advances in interactive world models have made it possible to generate controllable, dynamic environments from action inputs.
Benchmark compression reduces the often prohibitive time and cost of model evaluation by evaluating models on a small benchmark subset that closely matches…
Relative-position attention mechanisms offer compelling alternatives to RoPE across various transformer architectures, delivering superior performance in…
Vision-language models can interpret instructions and plan manipulation tasks, yet struggle when acting directly as robot policies.
Learning the continuous-time evolution of nonlinear dynamical systems relies on trajectory-based objectives, which require simulating the learned dynamics…
Decision models such as Jev have emerged as a highly promising paradigm for LLM agent systems, providing lightweight and reliable categorical reasoning for…
Multi-head attention in Vision Transformers (ViTs) allows independent attention patterns across heads, but without explicit regularization, heads may learn…
Residual connections underpin Transformers, but they implicitly tie the residual width carried through depth to the compute width used inside each layer.
Computer-use agents increasingly tackle tasks ranging from everyday desktop activities to long-horizon professional workflows.
Linear attention scales linearly with sequence length, but its finite-state memory can limit long-context retrieval.
Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible generation, and bidirectional attention, but…
다른 보도 Wes Roth
Molecular discovery requires navigating vast combinatorial search spaces while balancing competing biological objectives.
Training-free expert pruning makes two decisions: which experts to remove, and what the layer computes when its router still selects a removed expert.
LLM-based multi-agent systems (MAS) rely on collaboration among specialized agents to solve complex tasks.
With model weights fixed, we measure how much of an entity-replacement response transfers between English factual questions, beyond whether answers change.
Self-distillation methods for large language models with an exponential moving average (EMA) teacher already retain a history of student updates.
Human cognitive tasks let us compare language models with humans under controlled conditions.
Human working memory lets people maintain and update information. Large language models (LLMs) face a related demand when tracking updates within a single…