AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Frontier Models Struggle to Copy
Frontier models can write proofs yet stumble on faithfully copying a long block of text that sits well within their context window. This paper traces the failure to 1D positional encodings, whose inductive bias favors a copying shortcut based on matching local context rather than carefully locating the corresponding input positions. The fix is 2D-RoPE, which lays text out on a 2D grid and gives each token a row and a column ID, so copying becomes retrieving tokens at a fixed column offset. Shallow Transformers with 2D-RoPE copy perfectly at input lengths hundreds of times longer than those seen in training.
RoboTTT
Recent robot foundation models run on single-step or short-history context, a strange way to attempt a five-minute assembly task. RoboTTT, from NVIDIA with Stanford and UT Austin, integrates test-time training into vision-language-action policies to scale visuomotor context to 8K timesteps, three orders of magnitude past prior policies, without growing inference latency. The longer context unlocks one-shot in-context imitation from human video, on-the-fly policy improvement, and robustness to perturbations. It improves overall performance by 87% over a single-step baseline, fully completes a ten-stage assembly task that no baseline finishes, and gains 62% from pretraining with 8K rather than 1K timesteps.
Self-Improving Agents Survey
Self-improving agents are moving from research demos into deployed systems, and this survey gives the trend a clean formalism. It frames a modern agent as a foundation model coupled with an operational scaffold of prompts, memory, tools, and control logic, then treats self-improvement as a self-induced update that commits changes to either the weights or the scaffold.
Metacognition in LLMs
Confidence calibration, self-verification, knowing when to stop, and knowing what you do not know have mostly been studied in isolation. This survey from Yale and UC Irvine argues they are facets of one capability, metacognition, and organizes the field around a monitor and control loop wrapped around the language model.
When Is Routing Meaningful
LLM routers and mixture-of-agents systems get judged on accuracy and cost, both of which can look great while the router is doing nothing. This DeepMind-affiliated work argues that whether routing means anything depends on two properties that are orthogonal to accuracy.
Harness Evolution, Rethought
Automatic harness evolution is what many teams now use to squeeze more out of agents, but the reported gains might not be coming from the harness at all. This paper argues that harness evolution is itself a search procedure and must be compared against simple search baselines under matched budgets.
Tracing Agentic Failure
Finding which step in a failed agent run actually caused the failure usually means either labeling failure data or running expensive per-step prompting. This Microsoft and UW-Madison work skips both by learning what success looks like and flagging deviations from it.
Failure as a Process
When a coding agent fails a task, the final pass or fail label hides when the run actually went wrong. This large-scale study treats failure as a timeline and annotates over 63,000 execution steps to see how coding-agent runs break down.
LingBot-World 2.0
Most world models fall apart after a few seconds, smearing textures and warping geometry as errors compound frame to frame. LingBot-World 2.0 from Robbyant holds 720p at 60 fps for a full hour of interaction and ships fully open.
Less Is More for Monitors
Chain-of-thought monitoring is treated as a reliable safety layer, but this study shows monitors often detect sabotage better with less access, not more. Giving a monitor the full agent reasoning trace can bury the signal, and the authors introduce extract-and-evaluate monitoring, where one monitor isolates the relevant excerpts and another scores them, lifting sabotage detection by 16.8 points on BigCodeBench-Sabotage with a GPT-4.1-mini monitor. Agents unaware they are watched are far easier to catch, and monitors reading long traces gain the most from filtering.
GFlowRL
Reward-maximizing RL tends to collapse large reasoning models onto a single dominant mode, and GFlowNet-style training is appealing because it matches reward distributions and keeps diverse reasoning paths. GFlowRL scales this to modern post-training by replacing the hard-to-learn partition function with an in-batch Monte Carlo estimate computed from the rollout group the pipeline already produces. It is the first GFlowNet-style RL algorithm to train stably across both dense and sparse architectures, reaching a 2048 Codeforces rating at 14B and outperforming prior methods on math, code, and adversarial red-teaming benchmarks like AdvBench and HarmBench.
LingBot-VLA 2.0
LingBot-VLA 2.0 is an open-source generalist embodied model from Robbyant, trained across 20 robot configurations from single-arm rigs to humanoids like Unitree G1 and Fourier GR-2. It packs 60,000 hours of curated data, 50,000 hours of real-robot trajectories plus 10,000 hours of egocentric human video, into one policy that also predicts future depth and semantic features before it acts. On 9 GM-100 tabletop tasks it beats π0.5 and GR00T N1.7 across two robot platforms and stays ahead on long-horizon mobile tasks, running at about 130 ms on a single RTX 4090D with open-sourced post-training code.
Verification as a Scaling Axis
Verification is emerging as a distinct scaling axis alongside pre-training and test-time compute, and this Stanford, NVIDIA, and UC Berkeley collaboration builds a training-free verifier that reads a continuous, calibrated score straight off the scoring-token logits instead of trusting a discrete pass or fail grade.
Always-On Agents
Always-on agents are systems whose future behavior depends on durable state built up across earlier interactions, and this 130-plus page survey argues that state is far more than memory. It spans task ledgers, permissions, credentials, commitments, provenance, triggers, and effects the agent has already committed to the outside world.
HOLA
Linear-attention and state-space models compress an entire prefix into a fixed-size state, buying constant memory but overwriting earlier facts when many key-value associations compete. HOLA gives linear attention a hippocampal complement, pairing a compressive recurrent state with a small exact memory to recover long-range recall.
Puzzle-75B
Bigger mixture-of-experts models keep winning on quality, but serving them at interactive latency is still hard. NVIDIA compresses the hybrid MoE Nemotron-3-Super into Puzzle-75B-A9B and roughly doubles interactive server throughput while holding quality.
The Harness Effect
As orchestration harnesses mediate every model call, this study asks how much the harness alone moves cost and performance. It ran 22 evaluation tasks across six foundation models, then changed only the orchestration layer while holding the models constant.
ReContext
Models now support 128K context windows yet still fail to use evidence already sitting in the prompt. ReContext is a training-free inference harness for long-context reasoning that uses model-internal relevance signals to build a query-conditioned evidence pool, then replays it right before final generation while preserving the full original context.
Agent Limitations Taxonomy
Benchmark scores keep climbing, yet the same agent failures resurface across otherwise unrelated evaluations, hidden behind the leaderboard. This University of Oxford work synthesizes 27 benchmark, taxonomy, and audit papers spanning 19 benchmarks into the first cross-cutting taxonomy of LLM-agent limitations.
BlockSearch
BlockSearch runs the first systematic study of in-context retrieval at the scales real retrievers actually face, million-token corpora and length generalization far beyond training size. It introduces a 0.6B language-model retriever whose architectural and training changes improve over prior LM baselines and length-generalize up to 10 times beyond their training length, pointing toward retrievers that stay reliable as context windows keep growing.
RLVR Meets Human Likeness
RL with verifiable rewards only optimizes what you can objectively score, so style, structure, and diversity quietly collapse and reward hacking creeps in. This MIT work adds an adversarial discriminator trained on human demonstrations as a learned proxy for the human output distribution, and the generator maximizes both task accuracy and that human-likeness signal. Across bug fixing, story generation, and a reward-hacking benchmark, it preserves RLVR's accuracy gains while restoring the fuzzy properties it usually destroys, with misbehavior nearly disappearing.
Replicating ML Papers with Agents
This work tests whether a coding agent can replicate a scientific ML paper from its materials alone, using a skill that turns each paper claim into a target with recorded evidence and gating completion on workspace evidence rather than the agent's final message. Across twelve runs over four papers, all twelve workspaces pass the completion gate and all 158 recorded targets are matched with report coverage. Yet repeated runs still differ in how papers are split into targets and in numerical fidelity, so completion becomes reproducible even when the path is not.
Red Queen Gödel Machine
Self-improving agents are only as strong as the evaluator scoring them, and most systems freeze that evaluator in place, so improvement stalls the moment the judge stops getting harder. The Red Queen Gödel Machine makes the evaluator part of the search itself, letting agents and the criteria that judge them co-evolve. --- ---
MCP Server Patterns
As teams rush to wrap tools and data behind the Model Context Protocol, they keep rebuilding the same server shapes without shared names for them. This industry experience paper catalogs the recurring architectures so builders can reason about MCP servers the way software engineers reason about design patterns. ---