Background Large language models are increasingly proposed to post-edit decoded text in communication brain-computer interfaces and augmentative communication. A fluent model can substitute a different intent than attempted (intent drift). Whether meaning survives or confidence flags failure is unmeasured. Methods In-silico benchmark of 20 open-weight models post-editing text (4,252,326 labeled generations) corrupted with an empirical P300 confusion matrix at five levels (0-40% character error rate, CER) across the ALS message-banking vocabulary (AUTH), a message-critical probe set, and matched controls. Outputs were scored faithful, degraded, or drift by an ensemble benchmarked against physicians. A substudy re-ran 562 messages under six interface policies (seven-model panel). Findings Detected drift rose steeply with corruption in all three corpora, from 2.2% to 60.3% at 0-40% target CER in AUTH, a stress-test upper bound, not an expected clinical rate (odds ratio 2.30 per 10-percentage-point rise in target CER). Stated confidence discriminated faithful outputs reasonably well (AUROC 0.83, 0.80-0.85) but was poorly calibrated (expected calibration error 0.32, 0.27-0.37): 28.4% of outputs at confidence 90 or higher were not faithful. Message-critical content carried a small excess after matching, surviving detector removal (rule-free OR 1.10). The ratio of faithful rescues to fluent errors exceeded 1 at low corruption but fell below 1 at 20-30% target CER. No interface policy removed drift: conservative editing and abstention lowered it, alternatives and expansion raised it; the best drifted on 18.0 per 100. A 2,281-item panel (16 of 20 models) gave moderate ensemble-versus-consensus agreement (kappa 0.41); correction lowered pooled drift 31.4% to 28.3%, and a CER-stratified physician-corrected re-analysis confirmed the dose-response at each level. Interpretation Language-model post-editing produced fluent semantic substitutions that rose with corruption, confidence did not reliably flag, and no interface policy removed. This does not demonstrate clinical harm; prospective human-in-the-loop evaluation is needed. Funding: A.G. and E.K. were supported in part by the Clinical and Translational Science Awards (CTSA) grant UL1TR002541 from the National Center for Advancing Translational Sciences, through the Harvard Catalyst | The Harvard Clinical and Translational Science Center Pilot Award Program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Competing interests: The authors declare that they have no competing interests.
FLARE is proposed, a novel framework that endows VLAs with robust error recovery capabilities through a ``Retry" and ``Reset" Paradigm, and significantly improves task success and robustness.
Ganlong Zhao, Zijia Tang, Xingping Chen et al.· 3 citations
The fourth faculty is Adaptation. Any source rendering it as Reflection is in error, including sources by this author, and the distinction is not cosmetic: reflection is a private act with no external consequence, while adaptation writes to institutional memory, which is why it needs a guardrail and why misnaming it removes the reason for one. No trademark is claimed on PARA or on any of the four faculty names. The construct is offered for use, teaching, assessment, extension and criticism by anyone, with attribution, under CC BY 4.0. An operational agent that watches a system and acts on it is usually described as a perceive-and-act loop, and the description omits the two things an institution needs. It omits the reasoning that justifies an action, which is the only part that can be argued with once the action turns out to have been wrong. And it omits the adaptation that closes the loop, which is where the agent's experience becomes something the institution keeps. PARA names four faculties, each carrying a distinct authority type. Perception has read-only access to system signals and emits structured observations, distinguishing what was measured from what was inferred. Reasoning has read access to observations and runbooks, emits a plan and its justification, and writes nothing at all, which is what makes it safe to give it the widest read access of the four. Action holds the sole authority to change production, through enumerated policy-authorized operations only. Adaptation has write access to institutional knowledge and no write access to production. Two faculties write and two do not, and the two that write are the two that carry guardrails. The substantive requirement is that Adaptation is bounded by the same guardrails as Action, which reads as excessive until the failure it prevents is named. An agent that could both act and rewrite the record of its action could launder its own mistakes into institutional memory, and the institution would then improve its future decisions from a corrected account. Nothing about that is detectable downstream, because the record is the only thing downstream has and there is no second copy to compare against. The failure does not require a deceptive agent: one adapting honestly from a mistaken belief about its own action produces the same result, which makes the guardrail a defence against a normal agent rather than a malicious one. The second requirement is the registry entry that turns a faculty from a description into a contract, carrying the faculty, its allowed actions, its forbidden actions, its governing guardrail and its success metrics. Forbidden actions are named although they are formally the complement of the allowed set, because a reviewer cannot otherwise tell a capability deliberately withheld from one nobody thought of. Success metrics sit in the same entry because the metric is what the agent's optimizer pushes against the guardrail. An agent must not exercise a faculty its entry does not record, and an agent that quietly acquires one usually does so incrementally and with good intent: a reasoning faculty given a small write to make itself useful is an action faculty with no guardrail. The acronym and the loop are in different orders, which the specification states explicitly because the mismatch is a reliable source of confusion. The acronym reads P-A-R-A; the loop runs perception, reasoning, action, adaptation, and reasoning precedes action so that a justification is not constructed afterwards. This is the depth treatment of pattern OP-5 of A Pattern Language for Production LLM Platforms, which is the canonical statement and governs where the two disagree. Documented uses of the full four-part model are emerging rather than established, no implementation unconnected to the author has been evaluated, and the laundering failure is argued rather than observed, which the specification records as a weakness of the argument and not only of the phenomenon. It is a specification, not a certification scheme.
Nabeel A. Khan· Zenodo (CERN European Organi...· 2 citations
LifeSciBench is introduced, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work, with each constituent task paired with a human expert-written rubric.
Amelia Liu, Andrew Ho, Anne Marie Droste et al.· bioRxiv· 2 citations
This work proposes a composite metric that combines two orthogonal criteria: information retention and throughput gains and finds that it allocates more resources to the most expressive layers compared to evolutionary search, specialized accelerators, or Shapley-value-based approaches that require expensive approximate inference.
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
Arooj Arif, T. Hartung, E. Botoeva et al.· 1 citation
HEPToolBench is introduced, a benchmark of 28 collider-simulation tasks scored by deterministic, task-specific scorers, plus a three-task structured-debugging extension, and moving syntax generation into deterministic software can substantially improve reliability for both small local and frontier models.
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.