arXiv subjects

Jingyang Du

Publications and source records attributed to Jingyang Du.

4 recordsLinked to original sources

arXiv2026

Shared Context Candidate Scoring with Jointly Adapted Readout Networks

Repeated decisions over a shared document create two distinct opportunities: reusing document computation and adapting how question representations score candidate answers. We present Q64, a candidate-scoring system that preserves a frozen 64-layer Qwen3.8-27B text backbone, reuses its hybrid prefix state and intermediate document representations, and jointly trains a residual adapter with a four-layer decoder and candidate-conditioned readout. We used ZiYor, a self-evolving AI system, to explore and refine the Q64 design. We specified the scoring method, implemented the system, and evaluated its accuracy and latency. The final adaptation stage updates 88.66 million parameters using 8,192 selected training views. On a fixed 5,112-item evaluation combining ARC-Challenge, ANLI-R3, and stratified subsets of MMLU-Pro and CMMLU, joint adaptation improves four-benchmark macro accuracy from 66.46% to 74.32%. It remains below the native 27B baseline at 75.68% and AnyJev Qwen3-32B L0 at 76.68%. On a separate synthetic workload with 4,096 document tokens and 50 questions, median cold-request latency is 2.237 seconds, compared with 5.383 seconds for the native baseline and 56.872 seconds for the measured batched AnyJev configuration. Short single-question requests do not share this advantage. The evidence supports an accuracy-latency tradeoff in the evaluated systems, rather than a backbone reduction or an isolated component advantage. Public benchmarks informed earlier development, and real-document multi-question validation remains open.

cs.CL↗

arXiv2026

Exact Optimality Certification of QPLIB 2096 through Complete Set Partitioning

We present an exact computational proof that the frozen QPLIB_2096 instance has global minimum 7,025,720. The solution and certification approach was explored and refined through ZiYor, a self-evolving AI system. The authors provide the mathematical account and exact validation reported here. The original formulation has 300 binary variables, 6,900 triangle inequalities, and 25 nonconvex quadratic constraints. A coefficient-level reconstruction identifies its variables with the unordered pairs of 25 items. The triangle inequalities encode partitions, while each quadratic constraint becomes a bound on the total weight of edges incident to one partition block. This yields a set-partitioning formulation over all capacity-feasible blocks, with a separate edge-cost graph determining the objective. Exact enumeration examines 245,505 subsets of sizes one through six and finds 14,981 feasible blocks. Every six-element subset is infeasible; nonnegative capacity weights exclude all larger blocks. An integer price vector satisfies the dual inequality for every feasible block and has sum 2,229,456. Subtracting this sum from the total objective edge cost 9,255,176 gives a lower bound of 7,025,720, attained by the supplied six-block partition. Fresh solver-free checks validate the candidate against both frozen model formats and verify the certificate. The result is an instance-specific optimality proof with an exact primal and dual witness; no cross-instance performance conclusion is drawn.

cs.AI↗

arXiv2026

Construction-Reuse Trade-offs for Exact Certificates in Fixed-Rank Threshold Screening

Repeated threshold queries may reuse selected identities without reusing stale reports, but cheaper certificates need not shorten the complete response. We study selected-lower, atomic-upper (SLA) certificates for fixed-rank conjunctive screening with explicit missing-information semantics. An endpoint characterization and a counterexample separate same-source containment from policy-dependent online behavior. The original 320-session experiment reduces summed construction medians by 31.18% against an exclusion-cover certificate, yet its Cover/SLA full-API geometric time ratio is 0.9682 (95% conditional blocked interval 0.9593-0.9769), and SLA takes 12.94% more summed time than uncached Bitmap. Three separately launched complete repeats preserve this adverse ordering, with Cover/SLA ratios of 0.9665-0.9718. An additional 720-configuration exploration varies catalogue size, construction period, requested count and query locality on empirically resampled tables. SLA is faster in 295 configurations against Cover and 271 against Bitmap, descriptive counts that do not establish universal superiority. Separately instrumented additive costs distinguish construction savings from retrieval and report costs. A plane-stress component case adds an independent analytical displacement check and 4,608 boundary-challenging queries: five implementations agree exactly, while medium- and fine-mesh selections differ at 459 positions. A state-stratified public bolt-record exercise preserves 185 incomplete positions among 1,479 requests. Raw timings, complete configuration results and a tested clean-environment package support reproducibility. The SLA construction was explored and refined through the self-evolving AI system ZiYor; the named authors specified, implemented and evaluated it. This is a bounded mechanics-to-query study, not physical joint qualification or universal speedup.

cs.CE↗

arXiv2026

STC: Reversible Digit-Context Decomposition for BWT-Family Text Compression

Burrows-Wheeler-transform-based compressors rely on local context regularity, but structured text also contains dates, counters, identifiers, coordinates, and other digit runs whose values vary differently from their surrounding tokens. STC is presented as a new algorithm found by the authors through the self-evolving AI system zeelin. It is a practical BWT-family compressor that separates this source of variation before the component BWT stage. It replaces digit runs in the main stream with an unambiguous placeholder and stores the removed digits in length- and context-conditioned side streams. The side streams use stable bucket ordering and compact digit packing, so the decoder can reconstruct the original run order from the normalized main stream without storing a separate permutation. The resulting components are encoded by a fixed internal BWT/M03-style component coder. On enwik9, STC produces a 157,388,188-byte archive with a 183,174-byte decoder source package, giving a local LTCB-style total of 157,571,362 bytes. A full-enwik9 same-coder ablation shows that the digit-context decomposition reduces the archive by 2,629,561 bytes relative to the no-split control. The result is locally verified by full decode and SHA-256 matching; official benchmark status requires independent maintainer-side verification.

cs.IT↗