JoC-2026-001ResearchBreakthroughA breakthrough — a new result others can build upon.In peer reviewCleared the Commons round; under the AI panel and external review.
Shashank Taxak · AI & Machine Learning
We introduce Reverse Synthetic Networks (RSN), in which the data synthesizes the model in closed form: every weight is computed from corpus statistics, with no loss function, no gradient, and no GPU. We separate a positive and a negative result. For discriminative tasks a fully synthesized classifier edges a tuned linear SVM on 20 Newsgroups (87.0% ensemble) and reaches 90.8% on digit recognition with zero training, in seconds on a CPU. For generation, the same paradigm has a hard ceiling: a battery of twelve closed-form enrichments all collapse to a Modified Kneser-Ney n-gram. Measured honestly in bits-per-byte against a locally-run GPT-2, the pure RSN reaches 1.97 BPB versus GPT-2-small's 1.04, and a 100M-token scaling study shows the ceiling is representational, not data-limited. RSN is a cheap, strong floor for discriminative work, not a path to the trained generative frontier, and we explain why.
JoC-2026-004ResearchBreakthroughA breakthrough — a new result others can build upon.In peer reviewCleared the Commons round; under the AI panel and external review.
Wamique Hossain, Shashank Taxak · AI & Machine Learning
We present MCM-Guided Activation Steering (MCM-GAS), a framework that uses live financial market Ornstein-Uhlenbeck (OU) equilibrium dynamics to compute context-adaptive steering vectors for transformer residual streams. Unlike static activation steering---which injects a fixed mean-difference vector regardless of input context---MCM-GAS modulates each component of the steering vector in real-time using normalized z-scores from passively observed mean-reverting cryptocurrency pairs. We implement MCM-GAS on GPT-2 (117M parameters, 12 layers, d_model=768) and GPT-2 Medium (345M, 24 layers, d_model=1024) via TransformerLens residual stream hooks, computing four domain-specific steering vectors (code generation, bug fixing, architecture design, code explanation) from positive/negative activation poles. Across nine rigorous experiments totaling 700+ measurements, we demonstrate: (1) statistically significant representation shifts toward target activation poles (all p 1.5); (2) strict monotonicity of projection onto v_steering as a function of injection intensity alpha; (3) measurable market-driven vector modulation; (4) simultaneous attraction toward the positive pole and repulsion from the negative pole (all p < 0.005); and (5) MCM-GAS vectors that are statistically distinct from static vectors (p < 10^-33). Critically, our ablation study (Experiments 6-9) comparing MCM modulation against random, scrambled, and anti-correlated baselines reveals a vector- and model-dependent effect: on GPT-2, v_arch (layer 8) shows significant MCM advantage over random (p < 10^-12, Cohen's d = +6.04, 80th percentile); on GPT-2 Medium (345M), v_coding and v_bugfix show significant MCM advantage (d = +2.79, +6.16). At large-N (51 prompts), v_bugfix and v_arch replicate MCM advantage (d = +2.86, +4.86), while v_coding shows random advantage (d = -2.73). No single vector shows consistent MCM advantage across both models. These results establish MCM-GAS as a novel dynamically adaptive activation steering framework, with honest evidence that market modulation provides statistically significant advantages for specific representational subspaces, but the advantaged subspace is model-dependent rather than universal. Keywords: activation steering, representation engineering, Ornstein-Uhlenbeck process, market computation, transformer interpretability, residual stream injection, ablation study
