LimiX-2 tops three tabular benchmarks, chasing causes over scores
A Tsinghua-linked lab takes the table-model crown the day after Prior Labs wore it, storage gets an undo button for rogue agents, and a builder's 90-day memory test finds the failure where nobody was looking.
Wenzhun Intelligence and Tsinghua University's Cui Peng group released LimiX-2, a 400-million-parameter foundation model for structured data that now sits first on all three major tabular leaderboards. The team reports overall Elo ratings of 1935 on TabArena, 1432 on BCCO and 1506 on TALENT, placing it ahead of comparable models from Google, SAP and Amazon. The technical bet is unusual: instead of predicting a target column, LimiX-2 models the joint dependencies across every variable — a cell-level design its authors call Contextual Mechanism Networks — so classification, regression and missing-value imputation all become different queries against one learned structure. The company says the same machinery leads classic causal-discovery baselines on the Sachs and Causal Chamber datasets, and that the previous generation was validated across 3,800-plus enterprise scenarios with 60-odd strategic customers. It lands one day after Prior Labs' TabPFN-3.5 claimed the same territory — we covered that yesterday, TabPFN-3.5 tops the tabular benchmarks with a thinking mode — and the back-to-back releases are the clearest sign yet that tabular foundation models have become a real race, with the Chinese entrant pitching scaling laws and causal structure where the American one pitched reasoning. Paper and weights are public on arXiv and Hugging Face.
Cohesity introduced Agent Resilience, a backup-and-restore layer for enterprise AI agents that rolls an agent's memory and configuration back to a trusted state after it misbehaves. The pitch addresses a gap most agent-governance tooling leaves open: observability can flag that an agent went wrong, but nothing puts the databases, vector stores or settings it corrupted back. Cohesity's chief product officer Vasu Murthy put it plainly — detection can tell you an agent went off course, it cannot undo the changes. The service discovers the systems an agent touches, protects them alongside the agent's own memory, and draws a topology map of what each agent can reach; support covers AWS Bedrock agents first, with Microsoft and Google platforms on the roadmap and general availability expected by year end. The launch rides on Cohesity's fifth annual resilience survey of more than 3,200 IT and security decision-makers, where 55% said they are unprepared to detect or contain unintended agent actions and 69% reported a material cyberattack in the past twelve months, up from 54% a year earlier. Rollback is what guardrails look like when you assume the guardrail fails.
A builder on r/AI_Agents benchmarked an agent memory layer against the "just send the whole history" default over 90 simulated days, and the interesting result is not the 23-62x token saving — it is where memory lost. Across three synthetic corpora (companion chat, support desk, coding assistant), retrieving facts into a 600-token budget held context at 122-331 tokens per request while full history grew to about 7,500, and beat it on recall in two of three domains at 90 days — including 0.75 against 0.38 on the support corpus, evidence that long raw context degrades quietly. But at 30 days the memory system cratered on support (0.25 recall): the culprit was not retrieval but the update pass, which had silently archived a stored loyalty number when a vaguer restatement arrived, and overwrote a standing habit with a one-off sentence. The author's fixes — four deterministic guards on what may supersede what — took that cell from 0.25 to 0.875. Caveats are stated in the post itself: the author runs a memory product, each cell was a single run over 6-8 questions, and the corpus generator and bench are on GitHub. Small sample, self-interested author — but the finding that memory systems fail on identifiers and negations, silently, matches what agent builders keep reporting, and the repo lets anyone check.
If you ship an agent today, are you sending the full history and when did it start hurting — in cost or in recall? Tell us in the comments.
Sources: Mengram bench (GitHub)
