Flat machine learning models fit training-distribution statistics and degrade when that distribution shifts at test time. We present a cross-domain research arc demonstrating that hierarchical structured abstraction, which decomposes a prediction task into fewer, topologically-grounded decisions over domain-specific pr...
Results show that pre-verdict activations support prediction of susceptibility to candidate order and outperform the non-activation predictors evaluated here.
Hashmath Shaik, Gnaneswar Villuri, Alex Doboli· 0 citations
Reading an LLM judge's verdict from the logits of its first generated token is cheap, requires no generation, and is exactly what constrained decoding and likelihood-scoring evaluation harnesses produce. We show that this readout distorts position bias in one direction: it overstates it in every condition we test, so f...
Gnaneswar Villuri, Hashmath Shaik, Alex Doboli· 0 citations
A 0.6B language model, asked to verify 1,200 logical conclusions (half valid, half corrupted by a single semantic edit), answers YES every time. Judged by behavior it discriminates nothing; linear probes on its hidden states read the correct verdict at 0.96 AUC, transferring to unseen logical structures and separating...
Gnaneswar Villuri, Hashmath Shaik, Alex Doboli· 0 citations
LLM-based code generation fails when correctness depends on execution-dependent coupling: the meaning of one routine is defined by the runtime behavior of another, a relationship that cannot be resolved from textual descriptions alone. This limitation, which we call static binding, is not confined to explicitly coupled...
Gnaneswar Villuri, Hashmath Shaik, Alex Doboli· 1 citation
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