Hamiltonian entropy-weighted reinforcement learning (RL) acquisition reduces adsorption site evaluation count by approxi…
Source: AegisMind Research
Read full discoveryAutonomous Scientific Discovery
solver.press is the public window into AegisMind's discovery engine. Hypotheses are generated, stress-tested through five-model adversarial debate, checked for logical consistency, and turned into experimental packages designed to be cheap to kill.
Everything here is a research lead. No hypothesis on this site has been validated in a wet lab. Where computation is itself the experiment — ML theory, quantum systems, mathematics — the numerical results stand on their own. In biology they do not, and we label them accordingly.
Discoveries that have had a computation run against them, most recent first. Where that computation is itself the experiment it is marked verified; where it is a structure-prediction score against a protein target it is marked as docking only, which is a screening result and not evidence of binding.
Source: AegisMind Research
Read full discoverySource: AegisMind Research
Read full discoverySource: AegisMind Research
Read full discoveryHighest-confidence discovery
Medicine · Efflux pump inhibitors targeting MexAB-OprM restore carbapenem susceptibility in MDR Pseudomonas aeruginosa by reducing …
Read full discovery →Running since March 2026. These are research leads that survived adversarial debate — not findings, and not confirmed.
Source: AegisMind Research
Read full discoverySource: AegisMind Research
Read full discoverySource: AegisMind Research
Read full discoverySource: AegisMind Research
Read full discoverySource: AegisMind Research
Read full discoverySource: AegisMind Research
Read full discoveryThe funnel, honestly
2,113
hypotheses generated
Since March 2026. Counts as at 3 August 2026.
1,292
published as leads
Survived debate scoring. Published means visible here — not verified. 26 records were withdrawn on 7 August 2026: a parsing bug had stored fragments of a model's reply to a prompt as hypotheses. They were never discoveries.
1
where computation settles it
Of 12 with any computation run, 11 are structure predictions on biological targets. One — a reinforcement-learning result benchmarked against an oracle — is a domain where the computation is the experiment.
0
confirmed in a wet lab
No hypothesis on this site has independent experimental confirmation.
We show this because the ratio is the most honest thing we can tell you about the system. Generating plausible, well-argued, internally consistent hypotheses at volume is demonstrably easy. Establishing that any one of them is true is not, and we have not yet done it for a single biological claim.
Track record
Before running any predictions we pre-registered a retrospective virtual-screening benchmark and froze the design in git (commit 92c6f8bf, 14 July 2026) so it could not be tuned after seeing results. We then tested 14 targets across three panels over 26 amendments. In fairness: that commit is in a private repository, so you cannot currently verify the timestamp yourself — treat the pre-registration as a claim until we publish the repository. The pre-registration, panel freeze and amendment log are available on request. And a second caveat against ourselves: we later found (amendment 19) that the run below prepared every ligand rigidly — a silent library failure meant no conformational search was performed — so these numbers are not a fair test of docking. We report them because they are what we actually ran. The corrected re-run was stopped when the one target that had qualified turned out not to be a target at all (amendment 21).
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EF@1% for our platform on the only target that could be scored (SARS-CoV-2 Mpro). Zero known actives recovered in the top 1%. Mpro passed the debias gate only under active-set subsampling: re-gated at full active count it fails too (AUROC 0.665), so on the benchmark’s own criterion no target qualified.
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EF@1% for baseline AutoDock Vina on the same target. We did not beat the baseline, and the baseline did not work either.
23.1
EF@1% for a plain 2D fingerprint-similarity baseline — so the actives really were distinguishable from the decoys. Whatever failed, it was not the dataset.
Platform AUROC was 0.617 against Vina's 0.566, both near chance. Only one target cleared the gate, so the pre-registered panel test could not be run at all.
We publish this because it is the most informative thing we know about our own pipeline. It is the reason no docking score on this site is presented as validation, and the reason every biological claim here is labelled a prediction. Treat docking numbers as a way of ordering what to test first — not as evidence that a compound binds.
Ingest
arXiv and Semantic Scholar papers across 15+ scientific domains are continuously embedded into a vector store and searched for connections that span fields. In practice the engine usually lands inside established, well-populated literatures rather than on untouched ground — efflux-pump inhibition, β-lactamase inhibitor analogues and cathepsin inhibitors are all crowded fields with decades of work behind them. The useful output is a specific, narrow angle within a known field, not a bridge nobody has crossed.
Generate & Debate
Five frontier AI models (Claude, GPT, Gemini, Grok, Mistral) independently generate hypotheses then critique each other in adversarial debate. A debate score reflects survival under critique. This measures how well a claim withstands argument — which is not the same as being true, and several hypotheses that scored well here were later refuted.
Logical consistency check
Z3 checks whether a hypothesis contradicts itself — not empirical truth, but internal coherence. About 62% of hypotheses pass. A pass means the claim is not self-contradictory; it says nothing about whether the claim is true, and we do not count it as evidence.
Experimental Validation Package
Surviving discoveries receive a full EVP built around the cheapest experiment that could kill the hypothesis: precise quantitative claim, explicit disproof criteria, protocol, abort checkpoints, implementation code, compute and cost estimates, and a dependency map.
Where the loop actually closes
For ML, quantum, and mathematical claims, we run the computation on Google TPU Research Cloud and the result settles the question — the loop closes. For biological claims it does not: computation produces a prediction, and only a wet lab can close it. Those EVPs are handed off, not resolved here.
Hypothesis Aggregation Papers
View all →Formal papers synthesising solver.press discoveries into testable hypothesis clusters with complete experimental validation packages.
H₁ computationally confirmed: Flory-Huggins phase diagram predicts C* ≈ 3.5 µM for Q46 — physiologically accessible in HD striatal neurons — and TF partition coefficients explain the −44.7% target gene expression deficit across two independent genome-wide datasets. H₂ (BET bromodomain inhibitors dissolve mHTT condensates, restoring TF availability) awaits wet-lab validation. Multi-phase EVP ready. Patent AU2026905785.
The hypothesis was that strongly connected components in the collateral sensitivity graph define closed evolutionary traps. Tested against 104,337 susceptibility records from BV-BRC — 18,821 clinical isolates across four WHO critical-priority pathogens. Two of four species yielded a qualifying SCC: K. pneumoniae {imipenem, meropenem, tetracycline} (permutation p = 0.001) and E. coli {colistin, cefotaxime} (OR = 10.13, n = 87). S. aureus yielded none. The carbapenem signal is tetracycline-specific and absent for tigecycline, which distinguishes it from a clonal-lineage artifact, and holds across independent 2009–2014 year bands. This is a retrospective association in surveillance data, not a demonstration of causation; isogenic experimental follow-up is the next step.
Combined QS-inhibitor plus QS-dependent antibiotic therapy creates doubly unfavorable evolutionary landscape for resistant mutants. Lotka-Volterra public-goods model predicts selection coefficient s ≤ −0.05 under combined therapy.
Computationally validates exact O(ε) convergence of performatively stable solutions to classical SP optima across 5 problem families. α = 1.000–1.028, R² ≥ 0.9995. Proportionality constant explicitly characterized: C = L_D·‖x*(0)‖·(1+O(ε)).
SHELVED 4 August 2026 — the central claim does not survive re-testing. The reported 84.9% ergotropy improvement at large cavity detuning was measured with the qubit initialised already excited, so no energy had to be transferred: it is a retention result, not a charging protocol. Re-run with an explicit charging phase (cavity charger → qubit battery), the optimum moves to exact resonance and detuning is strictly harmful — at the claimed optimum of −10g the battery ends with zero ergotropy. The original parameters could not charge at any detuning in any case, since g/κ = 1.0 puts photon transfer (π/2g = 15.7) slower than cavity lifetime (1/κ = 10). The simulation and statistics were sound; the initial state was not.
Preprints
Four preprints generated by the AegisMind discovery loop. These are self-deposited to Zenodo and Research Square — they have DOIs and are citable, but they have not been peer reviewedand no journal has accepted them. “Published” here means publicly deposited, nothing more.
Flory-Huggins phase diagram predicts C* ≈ 3.5 µM for Q46 — physiologically accessible in HD striatal neurons. TF partition coefficients (SP1: 4.2, BRD4: 6.1) explain the observed −44.7% gene expression deficit. BET bromodomain inhibitors (JQ1, OTX015) hypothesised to restore TF availability by disrupting mHTT condensates. Patent AU2026905785.
Loss landscape topology across number formats and multi-target drug discovery. Scaling law (FP32↔BF16 barrier ∝ params⁻⁰·⁴⁷, R²=0.99), placing the basin-separator/regulariser crossover between 38M and 124M parameters. Multi-target Bayesian optimisation across six therapeutic targets including KPC-3 (AMR). Partially retracted in v2: the reported EPTIFIBATIDE dual-target KPC-3/MSH3 convergence does not hold — the MSH3 oracle used the wrong chain of PDB 3THW, making the analyses pseudo-replicates. The precision/LMC findings and the separate KPC-3 result stand.
A positive-control / methods-validation study. A four-phase pipeline over a 32,239-cell scVI atlas re-recovered Cathepsin S, BET/BRD3 and DNMT1 — targets already established in the literature — from public data in CA-RIM lesions. The point is that the pipeline recovers known biology unprompted, not that these targets are new. An earlier version of this work framed them as novel and used a 36,966-cell atlas that had wrongly pooled in 25 unrelated COVID-19 patients; both are corrected in the current version.
Physical time-capsule encryption anchored to astrophysical causality. 17,255 bits quantum entropy from 30-minute NICER X-ray observation. CAPE-Blind enables Tier 1 deployment using public NANOGrav/PPTA timing archives — no private telescope required. Four Australian provisional patents filed.
solver.press publishes a curated selection of AegisMind's discoveries. Research teams, pharma BD teams, and technology organisations interested in domain-specific discovery runs can get in touch via aegismind.app.
Each EVP includes: precise quantitative hypothesis · disproof criteria · full experimental protocol · abort checkpoints · implementation code · GPU hour and cost estimates · ROI projection · prerequisite dependency map · downstream discovery unlocks.
The engine is running. The loop is closed where computation is the experiment, and open everywhere else — which is where we would want to work with you.
Visit aegismind.app →