From 1a1cf8beea34973e3e70bade7f82e2c2f730e3a1 Mon Sep 17 00:00:00 2001 From: Ed_ Date: Mon, 22 Jun 2026 00:57:44 -0400 Subject: [PATCH] conductor(multiscale_hoffman): Phase 3 OCR - 63 frames OCR'd via winsdk in 3.0s --- .../artifacts/ocr.md | 1188 +++++++++++++++++ .../artifacts/phase2.log | 2 + .../artifacts/phase3.log | 2 + 3 files changed, 1192 insertions(+) create mode 100644 conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/ocr.md create mode 100644 conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/phase2.log create mode 100644 conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/phase3.log diff --git a/conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/ocr.md b/conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/ocr.md new file mode 100644 index 00000000..2bbaff1b --- /dev/null +++ b/conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/ocr.md @@ -0,0 +1,1188 @@ +# OCR Results + +## frame_00001.jpg + +``` +John Wheeler +the notes struck out on a piano by the observer- +participants of all places and all times, bits though +they are, in and by themselves constitute the great +wide world of space and time and things. +We enjoy many complex experiences. But for clarity, I focus on simple experiences. Consider an +agent that has only four distinct experiences: red, green, blue, and orange. +Notes +start Transition +OSO +``` + +## frame_00002.jpg + +``` +(no text extracted) +``` + +## frame_00004.jpg + +``` +Favorites +John Wheeler +the notes struck out on a piano by the observer- +participants of all places and all times, bits though +they are, in and by themselves constitute the great +wide world of space and time and things. +We enjoy many complex experiences. But for clarity, I focus on simple experiences. Consider an +agent that has only four distinct experiences: red, green, blue, and orange. +OSOS +``` + +## frame_00006.jpg + +``` +Minimal Observer-Participant +``` + +## frame_00010.jpg + +``` +Minima( Observer-Participant +``` + +## frame_00011.jpg + +``` +Experiences +``` + +## frame_00012.jpg + +``` +Markov Chain +o +.2 +.5 +.3 +.4 +oo +.3 +.2 +.4 +.2 +.1 +.2 +.1 +.3 +.4 +.2 +``` + +## frame_00013.jpg + +``` +gubgca(e Observer +``` + +## frame_00014.jpg + +``` +gubgca(e Observer +.49 .51 +.65 .35 +``` + +## frame_00018.jpg + +``` +Trace +o +oo +O +O +.49 +.65 +O +.51 +.35 +.2 +.5 +.3 +.4 +.3 +.2 +.4 +.2 +.1 +.2 +.1 +.3 +.4 +.1 +.2 +.1 +``` + +## frame_00019.jpg + +``` +Trace Formula +O +.2 +.5 +.3 +.4 +.3 +.2 +.4 +.2 +.1 +.2 +.1 +.3 +.4 +.1 +.2 +.1 +``` + +## frame_00020.jpg + +``` +O +O +.49 +.65 +.51 +.35 +Trace Order +O +O +.2 +.5 +.3 +.4 +.3 +.2 +.4 +.2 +.1 +.2 +.1 +.3 +.4 +.1 +.2 +.1 +``` + +## frame_00021.jpg + +``` +Hidden Memory/Contro(g: B, C, D +o +.2 +.5 +O +.3 +.4 +oo +.4 +.1 +.2 +.1 +A: Visible +C: Invisible +B: Exits +D: Entrances +C)-ID +``` + +## frame_00023.jpg + +``` +Trace Logic +The set of all Markov chains +form a nonBoolean logic +under the trace order. +``` + +## frame_00024.jpg + +``` +Trace Logic +For any Markov chain P, +the traces of P +form a Boolean sublogic +``` + +## frame_00025.jpg + +``` +Trace Logic +If P has n states +its Boolean sublogic +has 2n members +``` + +## frame_00026.jpg + +``` +Policy +A Markov matrix on +the trace logic +``` + +## frame_00027.jpg + +``` +Simple Example: Path Through Trace Logic +``` + +## frame_00028.jpg + +``` +Policy +Attention shifts +Scale shifts +Reparameterizations +Subsystem now driving policy +``` + +## frame_00029.jpg + +``` +Recursive Trace Logic +The collection of all policies +with their trace logic +``` + +## frame_00031.jpg + +``` +Intelligence Metric 12 +For any measure n there +are many Markov chains +for which IT is stationary, +with differing rates of convergence. +``` + +## frame_00032.jpg + +``` +Metric K and 12 +For Markov matrix M with +12 +convergence: +Tblind +K = log10 +``` + +## frame_00033.jpg + +``` +Multiscale Community Structure +For any measure 7T there are many +Markov chains for which IT is stationary, +with differing community structures. +Community structure is dictated by +eigenvectors with eigenvalues close to 1 +(i.e., slow mixing between communities) +``` + +## frame_00034.jpg + +``` +Po(icieg +stationary measures +``` + +## frame_00035.jpg + +``` +Trace Blanket +Construct self and world +Direct versus indirect control +``` + +## frame_00036.jpg + +``` +Bayes Rule +Meet of the trace logic +``` + +## frame_00037.jpg + +``` +Trace Logic / Spacetime +Relativistic spacetime +can be constructed +from the trace logic +``` + +## frame_00038.jpg + +``` +Time Dilation +``` + +## frame_00039.jpg + +``` +Time Dilation +Time runs slower on a trace +``` + +## frame_00040.jpg + +``` +Ato gap between ticks of big chain +``` + +## frame_00041.jpg + +``` +(no text extracted) +``` + +## frame_00044.jpg + +``` +DONALD HOFFMAN +Robert +``` + +## frame_00045.jpg + +``` +chrisfields +VALD HOFFMAN +Robert Chis-Cire +``` + +## frame_00046.jpg + +``` +. chrisfields +s'ALD HOFFMAN +Robert Chis-Cire +``` + +## frame_00047.jpg + +``` +ctrisfields +NALD HOFFMAN +Robert Chis-Cire +``` + +## frame_00048.jpg + +``` +Robert Chis-Cire +``` + +## frame_00049.jpg + +``` +chrisfields +Robert Chie-Cire +DONALD HOFFMAN +Chetan Prakash +``` + +## frame_00050.jpg + +``` +chrisfields +Robert Chie-Cire +DONALD HOFFMAN +Chetan Prakash +``` + +## frame_00052.jpg + +``` +Contents +Karl Friston +A free energy principle: on the +nature of things +of +Uni +AG 202 s +All by +of of +of +imply. +this +a witb +by +11.6.00 +2 +3 +4 +5 +6 +Introduction +I. I Cognition all the way down? +1.2 Not so fast . +The argument in a nutshell +A formal lexicon for efficient search in biological problem spaces +3.1 Problem spaces thc setup . +3.2 Problem spaces in unconventional biological cases +3.3 Intelligence qua search efficiency in problem space . +3.4 How search efficient is amoeboid chemotaxis and planarian regeneration? +lh•ee-energy geometry and search efficiency in biological problem spaces +'1.1 From operators to vector fields. . . , +4.2 Kinematic and thermodynamic roles of constraints , +4.3 Helmholtz decomposition and variational free energy +4.4 Logarithmic search efficiency and a Landauer•style bound . +4.5 Prediction horizon revisited +Blankets all the way down: Hierarchical renormalisation +of problem +spaces +5.1 Coarse-graining fast variables produces effective blankets +5.2 Renormalisexl problem spaces and additive search efficiency . . . . . +5.3 Ensemble variational free energy and thermodynamic entropy +Conclusion +3 +5 +10 +12 +12 +14 +15 +17 +20 +21 +21 +22 +24 +25 +``` + +## frame_00054.jpg + +``` +to its +of its +by i' r, a of +of .nhing in 'o +physiß i, e. +it only and +i' in +•he of will +"the is reL.tional .gdy +thing in +dye'] In +nplyuxg fm physio +'be kind of +or Al'hough established. +As fn« ts of +in i' 01 — +Of +of of +in like To of this +(e.g. things a +at d'stu•e• a +of patting +ing of At +physio 05 +all following +vmiple +Contents +2 +3 +4 +5 +G +Introd uction +I. I Cognition all the way down? +1.2 Not so fast . +The argument in a nutshell +A formal lexicon for efficient search in biological problem spaces +3.1 Problem spaces the setup . . +3.2 Problem spaces in unconventional biological cases +3.3 Intelligence qua search efficiency in problem space +3.4 How search efficient is amoeboid chemotaxis and planarian regeneration? +h•ee-energy geometry and search efficiency in biological problem spaces +'1.1 From operators to vector fields. . +4.2 Kinematic and thermodynamic roles of constraints , +4.3 Helmholtz decomposition and variational frec enerstv +Logarithtnie search efficiency and a Landauer-style bound . +1.5 Prediction horizon revisited +Blankets all the way down: Hierarchical renormalisation +spaces +5. I Coarse-graining fast variables produces effective blankets +5.2 Renorrnalised problem spaces and additive search efficiency . +5.3 Ensemble variational free energy and thermodynamic entropy +Conclusion +of problem +3 +5 +10 +12 +12 +14 +15 +17 +20 +21 +21 +22 +24 +25 +``` + +## frame_00055.jpg + +``` +Contents +2 +3 +4 +5 +G +Introd uction +I. I Cognition all the way down? +1.2 Not so fast . +The argument in a nutshell +A formal lexicon for efficient search in biological problem spaces +3.1 Problem spaces the setup . . +3.2 Problem spaces in unconventional biological cases +3.3 Intelligence qua search efficiency in problem space +3.4 How search efficient is amoeboid chemotaxis and planarian regeneration? +Fh•ee-energy geometry and search efficiency in biological problem spaces +'1.1 From operators to vector fields. . +4.2 Kinematic and thermodynamic roles of constraints , +•1.3 Helmholtz decomposition and variational frec enerkv +Logarithttlic, search efficiency and Landauer-style bound . +'I. 5 Prediction horizon revisited +Blankets all the way down: Hierarchical renormalisation +spaces +5.1 Coarse-graining fast variables produces effective blankets +5.2 Renorrnalised problem spaces and additive search efficiency . +5.3 Ensemble variational free energy and thermodynamic entropy +Conclusion +of prob lcm +3 +5 +10 +12 +12 +14 +15 +17 +20 +21 +21 +22 +24 +25 +1.7 +in +``` + +## frame_00056.jpg + +``` +Contents +_erv +1 gss. +2 +3 +4 +5 +G +Introd uction +I. I Cognition all the way down? +1.2 Not so fast . +The argument in a nutshell +A formal lexicon for efficient search in biological problem spaces +3.1 Problem spaces the setup . . +3.2 Problem spaces in unconventional biological cases +3.3 Intelligence qua search efficiency in problem space +3.4 How search efficient is amoeboid chemotaxis and planarian regeneration? +Fh•ee-energy geometry and search efficiency in biological problem spaces +2.1 +'1.1 From operators to vector fields. . +4.2 Kinematic and thermodynamic roles of constraints , +•1.3 Helmholtz decomposition and variational frec enerkv +Logarithttlic, search efficiency and Landauer-style bound . +'I. 5 Prediction horizon revisited +Blankets all the way down: Hierarchical renormalisation +spaces +5.1 Coarse-graining fast variables produces effective blankets +5.2 Renorrnalised problem spaces and additive search efficiency . +5.3 Ensemble variational free energy and thermodynamic entropy +Conclusion +of prob lcm +3 +5 +10 +12 +12 +14 +15 +17 +20 +21 +21 +22 +24 +25 +``` + +## frame_00057.jpg + +``` +Contents +a in +1 gss. +vmiple +2 +3 +4 +5 +6 +Introduction +I. I Cognition all the way down? +1.2 Not so fast. . +The argument in a nutshell +A formal lexicon for efficient search in biological problem spaces +3.1 Problem spaces thc setup . . +3.2 Problem spaces in unconventional biological cases . +3.3 Intelligence qua search efficiency in problem space +3.4 How search efficient is amoeboid chemotaxis and planarian regeneration? +h•ee-energy geometry and search efficiency in biological problem spaces +1.1 From operators to vector fields. . +'1.2 Kinematic and thermodynamic roles of constraints , +'1.3 Helmholtz decomposition and variational free energy +4.4 Logarithmic, search eflieiency and Landauer-style bound . +I. , 3 Prediction horizon revisited +Blankets all the way down: Hierarchical renormalisation +spaces +5. I Coarse-graining fast variables produces effective blankets +5.2 Renorrnalised problem spaces and additive search efficiency . +5.3 Ensemble variational free energy and thermodynamic entropy +Conclusion +of problem +3 +5 +10 +12 +12 +14 +15 +17 +20 +21 +21 +22 +24 +25 +``` + +## frame_00058.jpg + +``` +Contents +2 +3 +4 +5 +G +Introduction +1.1 Cognition all the way down? . +1.2 Not so fast. . +The argument in a nutshell +A formal lexicon for efficient search in biological problem spaces +3.1 Problem spaces thc setup . , +3.2 Problem spaces in uncomentional biological cases +3.3 Intelligence qua search efficiency in problem space . +3.4 How search efficient is amoeboid chemotaxis and planarian regeneration? +h•ee-energy geometry and search efficiency in biological problem spaces +3 +5 +10 +12 +12 +15 +17 +20 +4.1 From operators to vector fields. . +1.2 Kinematic and thermodynamic roles of constraints +•1.3 Helmholtz decomposition and variational free energv +4.4 Logarithtnic search eflieiency and a Landauer-style bound . +Prediction horizon revisited +Blankets all the way down: Hierarchical renormalisation +spaces +5.1 Coarse-graining fast variables produces effective blankets +5.2 Renorrnalised problem spaces and additive search efficiency . +5.3 Ensemble variational free energy and thermodynamic entropy +Conclusion +of problem +21 +21 +22 +24 +25 +19SS, +CONTENTS +``` + +## frame_00060.jpg + +``` +6 "-Sg:u +Contents +Introd uction +'o folm this to +of lays +n u y to path +'his a +tla• is a +of detail, In this +TIE is to a +While +-e Will a +gJ-.qyl +of biology +2 +3 +4 +5 +G +I. I Cognition all the way down? +1.2 Not sofast. . +The argument in a nutshell +A formal lexicon for efficient search in biological problem spaces +3.1 Problem spaces thc setup . . +3.2 Problem spaces in unconventional biological cases +3.3 Intelligence qua search efficiency in problem space . +3.4 How search efficient is amoeboid chemotaxis and planarian regeneration? +h•ee-energy geometry and search efficiency in biological problem spaces +3 +5 +10 +12 +12 +14 +15 +17 +20 +4.1 From operators to vector fields. . +'1.2 Kinematic and thermodynamic roles of constraints , +4.3 Helmholtz decomposition and variational frec enerkv +4.4 Logarithmic search efficiency and a Landauer-style bound . . +'l. , 3 Prediction horizon revisited +Blankets all the way down: wrarchical renormalisation +spaces +5.1 Coarse-graining fast variables produces effective blankets +5.2 Renormalised problem spaces and additive search efficiency . +5.3 Ensemble variational free and thermodynamic entropy +Conclusion +of problem +21 +21 +22 +24 +25 +``` + +## frame_00061.jpg + +``` +2 Allen Discovery Center, Tufts University, Medford, MA, USA +3 Wyss Institute for Biologically Inspired Engineering at Harvard University, +MA, USA +AV 6 16:SC2' +Boston, +this +fa"ly of +but to +of +ics_ d _ -r +by +e of this a +its stays plo-,l +is in a way +of 'his +will +first in a +of physio +n,is is to +where +Canadian Institute for Advanced Research, Program on Brain, Mind, and +Consciousness, fironto, Canada +A b stract +This paper provides a variational formalisation of biological intelligence as search +efficiency in multi-scale problem spaces, aiming to resolve epistemic deadlocks in +the basal "cognition wars." It extends classical work on symbolic problem-solving to +define a novel problem space lexicon and seamle efficiency metric. Construed as an +operationalisation Of intelligence, this metric is the decimal logarithm Of the ratio +between the cost of a random walk and that of a biological agent. Thus, the search +efficiency measures how many orders of magnitude of dissipative work an agentic +polic»; saves relative to a maximal-entropy search stratekv. Empirical models for +amoeboid chemotaxis and barium-induced planarian head regeneration show that +even 'simple' organisms—under conservative (i.e., intelligenceyunderv:stirnoting) as- +sumptions—are from two-hundred- to billion-trillion-fold more efficient in problem +space exploration. Embedding the discrete problem space calculus in stochastic +dynamics endowed with a Helmholtz deexompcy;ition connects efficient search with +gradient descent on variational free cnerkv and yields a universal Landaucr-style +bound; Each ten-fold increase in search efficiency requires a minimum thermody- +namic expenditure. Moreover, successive coarse-graining of fast variables generates +a 'blankets-all-the-way-down' hierarchy of renormalised problem spaces, where the +global, multi-scale intelligence is thc sum of search gains. Therefore, +our synthesis argues that the "mark Of the cognitive" is perhaps better sought in +the measurable efficiency With which living systems, from single cells to complex +organisms, traverse enerkV and inform, ion gradients to tame combinatorial explo- +sions—one Markov-blanketed problem space at a time. +Keywords: basal cognition; problem spaces; search efficiency; Free Energy +Principle; Markov blankets; biological intelligence. +``` + +## frame_00062.jpg + +``` +Taming Combinatorial Explosions: A Variational +Principle for Diverse Intelligence as Multi-scale +fairly of +of el.•, +ics. This siry +things - +of this a o i +is its stay. to physics Ving +is a way +is a by +will +with +Efficient Search +Robert Chis-Ciurel, Michael Levin2'3, +Anil K. Sethl," +Sussex Centre for Consciousne* Science, University Of Sussex, Brighton, United +Kingdom +2 Allen Discovery Center, 'Ihifts University, Medford, MA, USA +Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, +MA, USA +Canadian Institute for Advanced Research, Program on Brain, Mind, and +Consciousness, Toronto, Canada +Abstract +'I'llis paper a variational formalisation Of biological intelligence as search +efficiency in multi-scale problem spaces, aiming to resolve epistemic deadlocks in +the basal "cognition wars." It extends classical work on symbolic problem-solving to +define a novel problem space lexicon and seapeh efficiency metric. Construed as an +operationalisation of intelligence, this metric is the decimal logarithm of thc ratio +between the cost Of a random walk and that Of a biological agent. Thus. the search +efficiency measures how many orders Of magnitude Of dissipative work an agentic +policy saves relative to a maximal-entropy search strategy. Empirical models for +amoeboid chemotaxis and barium-induced planarian head regeneration show that +even •simple' organisms—under conservative (i.e., intelligence-underestimating) as- +from two-hundred- to billion-trillion-fold more efficient in problem +space exploration. Embedding the discrete problem space calculus in stochastic +dynamics endowed With a Helmholtz decompcxsition connects efficient search with +``` + +## frame_00072.jpg + +``` +iple +fairly of +but to +of in divi&mls A-thing fm +ways in which +This dye +things - +of this a o +is its physics. I ving +is a allows +two +is to by +will +. first a +phe• +tning +'he of physics +a •blankets-all-this-way-down' hierarchy Of renormalised problem spaces, where the +global, multi-scale intelligence is the Of scalc+pecific search gains. Therefore, +our synthesis argues that the "mark of thc cognitive" is perhaps better sought in +the measurable efficiency with which living systems, from single cells to complex +organisms, traverse enerky and information gradients to tame combinatorial explo- +sions—one Markov-blanketed problem space at a time. +Keywords; basal cognition; problem spaces; search efficiency; Free Energy +Principle; Markov blankets; biological intelligence. +Contents +2 +3 +ion +1.1 Cognition HII the way down? . +1.2 +The argument in a nutshell +A formal lexicon for efficient search in biological problem spaces +3.1 +3.2 +3.3 +Problem spaces— the setup . +Probiotn spaces in unconventional biological cases +Intelligence qua search efficiency in problem space +HOW search L'lfieient is amoeboid ehc•notnxis and planarian +2 +3 +5 +10 +``` + +## frame_00125.jpg + +``` +represent s +the entropy Of the i-th layer's marginal density over its coarse-grained State +iple +fairly of +to +of star + +y in "ich +ics. This dye +Ivy +things — +ning fm +of this a o +its physics I ving +is in a way alms +two +by +will a +in first a +manifold, with t) the normalised probability-density function over the phase space +for layer i. +(2019) (Eq. 10, 12) proves that, for an exchangeable ensemble at NFSS, the +ensemble average of particular variational free energies is proportional to thermodynamic +entropy. Specialising that result to our hierarchical stack yields: +(22) +factorisation weak coupling so that inter-level mutual informations vanish +to II). If residual correlations are present, a positive term '(X O); X(j)) must be added, +24 +where denotes an ensemble average over the stationary ensemble of coarse-grained +blankets, and ergodicity or, more weakly, mixing on theltimescale of olxservation, ensures +Thus, Ai) (t) are the dimensionless variational free energies (in nats) +for each level i. Equation (22) showcases how the information-theoretic and energetic +accounts Of adaptive search are two faces Of the same coin: Every decibel Of search +efficiency saved at level i (Section 4.2) corresponds to a marginal reduction in entropy +production costed at /t„Tln 10. Moreover, because blanket flows are gradient descent on +F(i), minimising search time anywhere in the hierarchy generally25 lowers S(t). In this +sense, as an intelligence metric, K recapitulates the principle of efficient self-organisation +that enables living systems to resist a wholesale increase in their own entropy, consistent +With the physics Of non-equilibrium systems. +``` + +## frame_00150.jpg + +``` +to to +fairly of +but to +of s:at+ A-thing +ay•s +This +"in* fm +ve of this , a o +its to plo"cs I Ving +is in a way +slight In +by +will +in Bay— in a +'II" ill +of physio +n,is i, of +``` + +## frame_00151.jpg + +``` +of el. +ics. This dye +may lm•fa +fm +ve of this a o +n•quin-d_ its Ving +is a way allows +is a to by +Mile will +first i" a +'he physics +``` + +## frame_00152.jpg + +``` +to foll«.v to +fairly of +to a +of A-thing el. +This dye +fm +ve of this a o +n•quin•_ its I Ving +is a uay allows +is a to by +Mile will +in first i" a +tning +Taming Combinatorial Explosions: A Variational +Principle for Diverse Intelligence as Multi-scale +Efficient Search +Robert Chis-Ciurel , Michael Levin2'3, +. Anil K. Sethi'4 +``` + +## frame_00153.jpg + +``` +to foll«.v to +fairly of +a of +of A-thing el. +This dye +fm +ve of this a o +n•quin•_ its I Ving +is a way allows +is a to by +Mile will +in first i" a +tning +Taming Combinatorial Explosions: A Variational +Principle for Diverse Intelligence as Multi-scale +Efficient Search +Robert Chis-Ciurel, Michael Levin2'3 +. Anil K. Sethi'4 +I Sussex Centre for Consciousn(ss Science, University of Sussex, Brighton, United +Kingdom +2 Allen Discovery Center, Tufts University, Medford, MA, USA +a Wyss Institute for Biologically Inspired Engineering at Harvard University, Ihston, +MA, USA +Canadian Institute for Advanced Research, Program on Brain, Mind, and +Consciousness, Toronto, Canada +A bstract +``` + +## frame_00154.jpg + +``` +• A. 6 16:53:" +Taming Combinatorial Explosions. A Variational +Principle for Diverse Intelligence as Multi-scale +fa"b• of Gys +to a +of +in which +This dye +by +tl.ingv— +slcwies fm +ve of this a o +n•quind its I ving +is a way +this +will +in Bay — in a +tning +n,is +en agy +Efficient Search +Robert Chis-Ciurel, Michael Levin2'3, +, Anil K. Sethi,' +I Sussex Centre for Consciousness Science, University Of Sussex, Brighton, United +K ingdorn +2 Allen Discovery Center, Tufts University, Medford, MA, USA +3 Wyss Institute for Biologically Inspired Engineering at Harvard University, Bcxston, +MA, USA +'t Canadian Institute for Advanced Research, Program on Brain, Mind, and +Consciousness, Toronto, Canada +A bstract +This paper provides a variational formalisation Of biological intelligence as search +efficiency in multi-scale problem spaces, aiming to resolve epistemic deadlocks in +the basal "cognition wars." It extends classical work on symbolic problem-solving to +define novel problem space and scareh efficiency metric. Construed as an +operationalisation of intelligence, this metric is the decimal logarithm of thc ratio +between the cost Of a random walk and that Of a biological agent. Thus, the search +efficiency measures how many orders Of magnitude Of dissipative work an agentic +``` + +## frame_00157.jpg + +``` +chis fields +Robert ChB-Cire +DONALD HOFFMAN +h etan Prakash +``` + +## frame_00158.jpg + +``` +chrisfields +Robert Chis-Cire +DONALD HOFFMAN +hetan Prakash +``` + +## frame_00159.jpg + +``` +chrisfields +DONALD HOFFMAN +Robert Chis-C +``` + +## frame_00160.jpg + +``` +chrisfields +Robert Chis-Cire- +DONALD HOFFMAN +Chetan Prakash +``` + +## frame_00161.jpg + +``` +chrisfields +Robert Chis-C +DONALD HOFFMAN +Chetan Prakash +``` + +## frame_00162.jpg + +``` +chrisfields +Robert Chis-Cire +DONALD HOFFMAN +Chetan Prakash +``` + +## frame_00163.jpg + +``` +Michael Levh +chrisfields +Robert Chis-Cire +DONALD HOFFMAN +Chetan Prakash +``` + +## frame_00164.jpg + +``` +chrisfields +Robert Chis-Cire +DONALD HOFFMAN +Chetan Prakash +``` + +## frame_00165.jpg + +``` +Robert Chis-Ciie +Chetan Prakash +``` diff --git a/conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/phase2.log b/conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/phase2.log new file mode 100644 index 00000000..de8eb984 --- /dev/null +++ b/conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/phase2.log @@ -0,0 +1,2 @@ +Phase 2 Keyframes for C:\projects\manual_slop\conductor\tracks\video_analysis_multiscale_hoffman_20260621\artifacts\video.mp4 + OK: kept 63 frames diff --git a/conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/phase3.log b/conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/phase3.log new file mode 100644 index 00000000..931fac52 --- /dev/null +++ b/conductor/tracks/video_analysis_multiscale_hoffman_20260621/artifacts/phase3.log @@ -0,0 +1,2 @@ +Phase 3 OCR for C:\projects\manual_slop\conductor\tracks\video_analysis_multiscale_hoffman_20260621\artifacts\frames (winsdk) + OK: OCR'd 63 frames in 3.0s