Our Research
We study when an expensive or conventional AI computation can be replaced by an alternative—and what evidence is required to accept or reject that substitution.
Research philosophy
Evidence before substitution
Our research explores computational alternatives in AI, with an emphasis on checkable guarantees, declared evaluation criteria, and transparent negative results. We distinguish formally proven substitutions from empirical hypotheses and resource-limited outcomes.
We research efficient alternatives to conventional AI computation and demand explicit evidence before accepting the substitution.
Featured Research
Formal results supported by independently checkable evidence and explicit scope conditions.
Proof-Carrying Optimality for Finite Identification under Bounded Adversarial Answer Errors
Vikram Lex
Zenodo · Under Review at JMLR
Develops proof-carrying methods for finite identification when answers may contain a bounded number of adversarial errors. Candidate construction is separated from independently checkable optimality certificates: all 30 primary cells receive two-sided proofs, while 300 of 303 declared sweep cells receive proofs and three are explicitly reported as resource-limit outcomes.
Scope: explicit finite behavior tables and declared probe alphabets. Verification is polynomial in the explicit table and supplied proof, not necessarily in a succinct game description.
Empirical Studies
Measured alternatives evaluated against declared quality, resource, and transfer criteria.
Bounded-Horizon Local Transformer Training on CPUs: Quality, Throughput, and Memory
Vikram Lex
Research Square · Under Review at JAIR
Tests bounded-horizon local training as a CPU-constrained alternative to full-horizon Transformer training. Async RGC measured 1.382× BP throughput with higher memory use, but the prespecified held-out 1% non-inferiority criterion and TinyStories transfer criterion failed; readout equivalence was not established.
The throughput comparison used 29 stage-worker threads for RGC and 24 BP intra-op threads; measured PSS increased from 1.94 to 4.31 GiB.
Early Explorations
Preliminary directions reported with their present limitations and without product or advantage claims.
Quantum Hybrid Modules for AI: Attention, Optimization, and Verification on Near-Term Quantum Hardware
Vikram Lex
Quantum Machine Intelligence (Springer Nature) · Under Review
Explores quantum-classical modules for AI search, optimization, attention, and kernel methods. This is an early exploratory preprint; its current results do not establish a practical or end-to-end quantum advantage, and it does not represent a current product direction.