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...
The order in which candidate responses are presented can change an LLM judge's verdict. Detecting such a position flip ordinarily requires judging each pair in both orders, which doubles the number of judgments. We investigate whether residual stream activations recorded immediately before the initial verdict can predi...
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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