Why Language Models Hallucinate
The paper argues that hallucinations are not mysterious glitches but the predictable result of how LLMs are trained and evaluated. Pretraining creates statistical pressure to make errors, and post-training benchmarks often reward confident guessing over honest uncertainty. The fix is to realign mainstream evaluations to stop penalizing abstentions.
Disentangling the Factors of Convergence between Brains and Computer Vision Models
Large self-supervised ViTs trained on natural images develop brain-like internal representations. This paper teases apart what drives that convergence by varying model size, training amount, and image type in DINOv3, then comparing model activations to human fMRI (space) and MEG (time) with three metrics: overall linear predictability (encoding), cortical topography (spatial), and temporal alignment (temporal). Result: all three factors matter, and alignment unfolds in a consistent order from early sensory to higher associative cortex.
Universal Deep Research
Proposes a general, model-agnostic deep-research agent that lets users “bring your own model and strategy.” Instead of a fixed pipeline, UDR compiles natural-language research strategies into executable code, runs them in a sandbox, and emits structured progress notifications before returning a final report.
Visual Story Telling
A system and design framework that lets writers edit stories by acting directly on visuals of characters, locations, and timelines. Instead of only prompting, authors drag, connect, and reorder visual elements; the tool proposes synchronized text edits and can regenerate passages from the visual skeleton.
rStar2-Agent
rStar2-Agent is a 14B math-reasoning model trained with agentic RL that learns to think smarter by using a Python tool environment, not just longer CoT. It introduces GRPO-RoC, a rollout strategy that filters noisy successful traces, plus infrastructure for massive, low-latency tool execution. In one week and 510 RL steps on 64 MI300X GPUs, the model reaches frontier-level AIME while producing shorter solutions and showing transfer beyond math.
Adaptive LLM Routing
A routing framework that learns online which model to call for each query while honoring a spend limit. It treats routing as a contextual bandit, initializes with human preference data, and adds an online cost policy that allocates budget across queries.
Implicit Reasoning in LLMs
This survey defines implicit reasoning as multi-step problem solving that happens inside a model’s latent states without printing intermediate steps. It organizes the field by execution paradigm rather than representation format, and reviews evidence, evaluation, and open challenges.
On the Theoretical Limitations of Embedding-based Retrieval
Single-vector dense retrievers cannot realize all possible top-k relevance combinations once queries demand sufficiently many “mix-and-match” document sets. The paper ties this failure to the sign-rank of the relevance matrix, proves lower and upper bounds on the embedding dimension needed, and then stress-tests models with a simple but adversarially combinatorial dataset (LIMIT).
Self-Evolving Agents
This survey reviews techniques for building self-evolving AI agents that continuously adapt through feedback loops, bridging static foundation models with lifelong adaptability. It introduces a unified framework, covers domain-specific strategies, and discusses evaluation, safety, and ethics in advancing autonomous agentic systems.
Hermes 4
Hermes 4 introduces a family of hybrid reasoning models that integrate structured multi-turn reasoning with broad instruction-following. The report details data and training challenges, evaluates performance across reasoning, coding, and alignment tasks, and publicly releases all model weights.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack