AI Research Laboratory15 records
Computation
A domain view: everything tagged Computation.
- PaperPAP-2026-0005Paper 05 — Memory, algorithm libraries and reusable solution pathsAlgorithms, tools, contracts, costs and outcome history become capability nodes; Reuse ≻ Adapt ≻ Create.
- PaperPAP-2026-0006Paper 06 — Substrate-neutral computational containersAny execution environment with representable input, valid transition and readable output is a compute container; algorithms and containers are scheduled jointly.
- TheoryTHY-2026-0001Asymmetric spacetime tension: temporally heterogeneous world knowledgeAsymmetry is lifted from edge direction or weight to the effective time scale of nodes and relations. Each node carries stability, information-decay rate, update tension, system impact and local valid time; refresh is triggered by tension, external disturbance, information age, change velocity and event relevance rather than by one global clock, so stable knowledge sleeps, volatile knowledge refreshes often, and a rarely changing high-impact node triggers wide dependency recomputation when it does change.
- TheoryTHY-2026-0007Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateBeyond facts, the system keeps the algorithms, tools, execution contracts, costs, versions, applicability conditions and success/failure history it has used, and prefers reusing a known solution path over adapting one over creating one. Algorithms and compute containers are different layers: any environment that accepts representable input, performs a valid state transition and returns readable output is a container with its own cost, latency, error and availability model, so algorithm/container pairs are selected jointly.
- ExperimentEXP-2026-0103XA-06 first real-model pilot — Qwythos-9B-v2 on the A0→A5 ladder (36 trials, 2026-09-03)The first time a real model was placed inside the IPM instrument: hf.co/empero-ai/Qwythos-9B-v2-GGUF:Q4_K_M served by Ollama on an RTX 3070, run locally on 2026-09-03 by Splice (Claude Code) on Neo.K's authorization through XA-06L — MATH-003, CODE-001, CON-003 × A0–A5 × 2 replicates, 36 trials, 186 invocations, 162 trajectories, telemetry complete on every trial. Sealed REAL_MODEL_PILOT_INCOMPLETE: 33/36 complete, three trials aborted because the same-model verifier returned non-JSON to a strict parser (a real model behaviour, deliberately not re-rolled). SSR = 1.0, SDR = 0.0: scaffolding brought no measured quality gain on these three easy tasks while A5 used 3.10× the device energy and 3.13× the wall time of A0, and the fixed eight-sample conditions A2–A4 used 7.9–9.3×. The apparent A3/A4 quality rise to 0.667 is a missingness artifact; and for two of the three tasks the recorded 'quality' was output-format compliance, not task correctness (post-hoc: 33/33 completed outputs semantically correct; strict output-contract compliance 12/36).
- ProgramPRG-2026-0101Intelligence Physical Metrology (IPM)A research program that asks not how smart an AI is but what one answer costs a physical system: how many user turns, generation trajectories and hidden loops it took, how much effective semantic work was done, how much energy and computational spacetime was occupied, how much external scaffolding was leaned on, and how much verifiable quality came back. Ten theoretical papers (2026-09-02) define the measurement objects — task, quality, semantic work, physical computation, scaffolding capability, measurement metadata — and five falsifiable propositions; the v0.2 experimental protocol (Experiment A, single-pass vs scaffolded) is instantiated as the XA-02…XA-06L instrument packages and was executed once on a local 9B model.
- ResearchRES-2026-0103Capability line — how much intelligence remains without the loop, and the unified intelligence eventPapers 09–10 and the v0.2 Experiment A instrument. A system is (model, scaffolding vector); single-pass quality Q_SP and full-system quality Q_F give the scaffolding survival ratio SSR = Q_SP/Q_F, the dependence ratio SDR, the scaffold cost multiplier SCM and marginal scaffolding yields along a controlled ablation ladder A0…A5 — all read together with the physical overhead, because a loop is not cheating; hiding its cost is. Paper 10 packs quality, semantic work, physical computation and scaffolding into the canonical intelligence event, compares systems on Pareto frontiers under a no-premature-scalarization rule, and fixes a minimum reporting standard. The line's first real data point is the 2026-09-03 pilot on a local 9B model.
- SystemSYS-2026-0101XA-03 — telemetry and run logger (physical execution evidence layer)Provider-agnostic logger that records one benchmark run as append-only events plus telemetry and derives a typed summary deterministically: model-invocation, trajectory, tool-call and verifier spans, candidate created/discarded accounting, wall time, device-time, GPU power integrated to device_energy_j (energy type device_measured, never relabelled as marginal), peak memory and memory residency, with unknowns kept null (Unknown ≠ 0) and forbidden conversions (tokens → J, TDP → J, price → J, GPU-hours → J). Golden fixture 450 J / 1.75 util·s / 12 GB peak / 33 GB·s residency verified; 35 passed, 1 skipped in 0.39s; instrumentation burden is calibrated and reported, not subtracted.
- PaperPAP-2026-0101Paper 01 — What is a 'single turn', really? User turns, hidden loops, and the redefinition of single-pass intelligenceSeparates the chat-interface turn from model invocation, generation trajectory, agent loop and physical computation; defines externally loopless intelligence, a five-kind loop taxonomy and the single-pass condition U=1, G=1, R=1, L=0, S=0; introduces the event vector (Q, U, G, I, L, R, S, T, E, V_CST) and twelve invariants, starting with 'interaction compression ≠ computation compression'.
- PaperPAP-2026-0104Paper 04 — From neurons to joules: energy, thermodynamics, and physical lower bounds of intelligent computationBuilds the energy account bottom-up the way neural energetics does (ion flux → ATP → joule), shows that a spike — and therefore a token or a μI — has no fixed energy, types energy as gross/baseline/marginal/attributed with a declared boundary, and keeps Landauer's kT ln 2 as a bound on erasure rather than the price of reasoning.
- PaperPAP-2026-0105Paper 05 — Computation is more than FLOPs: memory, interconnect, hardware occupancy, and computational spacetime volumeReplaces FLOPs with a physical cost vector (typed ops, memory traffic by hierarchy, I/O, interconnect, residency, occupancy, time, energy), defines computational spacetime as a vector-first measure V_CST = ∫ R(t) dt with its own topology and peak footprint, and fixes CST measurement grades and boundaries; roofline and memory-wall results are the engineering backbone.
- ResearchRES-2026-0101Execution and physical line — what one answer costs in turns, semantic work, energy and computational spacetimePapers 01–05. Decomposes the chat-interface 'turn' into user turns, generation trajectories, model invocations, external loops, retries, selection and a physical execution trace; proposes the minimum intelligent semantic execution unit μI as the missing middle layer between physical primitives and task achievement; borrows the cross-level, latent-inference, population-coding and causal-perturbation method of neuroscience; types energy as gross / baseline / marginal / attributed with a declared boundary and keeps Landauer as a bound, not a price; and replaces FLOPs with a physical cost vector and a vector-first computational spacetime with its own topology.
- TheoryTHY-2026-0101Turn decomposition and externally loopless intelligenceA 'turn' is decomposed into the vector T = (U user turns, G generation trajectories, I model invocations, L external feedback loops, R retries, S selection/verification, P physical execution trace). The cleanest single pass is U=1, G=1, R=1, L=0, S=0, and it excludes only action→new-evidence→replanning, not autoregressive sequential computation. Loops come in five kinds (tool, environment, verifier, retry, candidate/selection), capability lives in four layers (intrinsic, elicited, system, product), and every 'one-turn' claim projects onto the event vector (Q, U, G, I, L, R, S, T, E, V_CST).
- TheoryTHY-2026-0104Energy accounting hierarchy and thermodynamic type safetyNeural energetics builds energy bottom-up (membrane dynamics → ion flux → pump work → ATP → dissipation) and finds that a spike has no fixed energy and that most cortical signaling energy is spent on synaptic integration and state maintenance, not the visible pulse. IPM copies the discipline, not the numbers: energy is typed as E = (gross, baseline, marginal, attributed, thermodynamic minimum), a boundary and baseline rule must be declared, information per Joule is not intelligence per Joule, Landauer's kT ln 2 bounds erasure and is not the price of a μI, and Shannon or variational 'energies' never become physical Joules without an explicit mapping.
- TheoryTHY-2026-0105Physical computation cost vector and computational spacetimePhysical cost is the vector C_P = (typed operations, memory traffic by hierarchy, I/O, interconnect, memory residency, device occupancy, wall time, energy), not FLOPs. Computational spacetime is first a measure V_CST = ∫ R(t) dt = (V_C, V_M, V_N, V_S) over a resource field; the components may not be added before a declared normalization, and equal volume (8 GPU × 10 s = 1 GPU × 80 s) is not equal topology Θ_CST = (T_wall, T_serial, P_parallel, D_peak, M_peak, B_peak, Γ_comm). Roofline, memory-wall and data-movement results explain why same-FLOPs workloads differ in time and energy; a peak hardware footprint is a capacity barrier; CST grades run from D (spec estimate) to A+ (causal resource attribution).